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Liu Y, Jiang N, Zou Z, Liu H, Zang C, Gu J, Xin N. The Solid Volume Ratio is Better Than the Consolidation Tumor Ratio in Predicting the Malignant Pathological Features of cT1 Lung Adenocarcinoma. Thorac Cardiovasc Surg 2024. [PMID: 39106958 DOI: 10.1055/a-2380-6799] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 08/09/2024]
Abstract
BACKGROUND More effective methods are urgently needed for predicting the pathological grade and lymph node metastasis of cT1-stage lung adenocarcinoma. METHODS We analyzed the relationships between CT quantitative parameters (including three-dimensional parameters) and pathological grade and lymph node metastasis in cT1-stage lung adenocarcinoma patients of our center between January 2015 and December 2023. RESULTS A total of 343 patients were included, of which there were 233 males and 110 females, aged 61.8 ± 9.4 (30-82) years. The area under the receiver operating characteristic (ROC) curve for predicting the pathological grade of lung adenocarcinoma using the consolidation-tumor ratio (CTR) and the solid volume ratio (SVR) were 0.761 and 0.777, respectively. The areas under the ROC curves (AUCs) for predicting lymph node metastasis were 0.804 and 0.873, respectively. Multivariate logistic regression analysis suggested that the SVR was an independent predictor of highly malignant lung adenocarcinoma pathology, while the SVR and pathological grade were independent predictors of lymph node metastasis. The sensitivity of predicting the pathological grading of lung adenocarcinoma based on SVR >5% was 97.2%, with a negative predictive value of 96%. The sensitivity of predicting lymph node metastasis based on SVR >47.1% was 97.3%, and the negative predictive value was 99.5%. CONCLUSION The SVR has greater diagnostic value than the CTR in the preoperative prediction of pathologic grade and lymph node metastasis in stage cT1-stage lung adenocarcinoma patients, and the SVR may replace the diameter and CTR as better criteria for guiding surgical implementation.
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Affiliation(s)
- Yu Liu
- Department of Thoracic Surgery, PLA 960th Hospital, Jinan, China
| | - Ning Jiang
- Department of Thoracic Surgery, The Second Hospital of Shandong University, Jinan, China
| | - Zhiqiang Zou
- Department of Thoracic Surgery, PLA 960th Hospital, Jinan, China
| | - Hongxiu Liu
- Department of Medical Imaging, PLA 960th Hospital, Jinan, China
| | - Chuanhang Zang
- Department of Thoracic Surgery, PLA 964th Hospital, Changchun, China
| | - Jia Gu
- Department of Pathology, PLA 960th Hospital, Jinan, China
| | - Ning Xin
- Department of Thoracic Surgery, PLA 960th Hospital, Jinan, China
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2
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Abstract
Screening with low-dose computed tomography has been shown to decrease lung cancer mortality. However, the issues of low detection rates and false positive results remain, highlighting the need for adjunctive tools in lung cancer screening. To this end, researchers have investigated easily applicable, minimally invasive tests with high validity. We herein review some of the more promising novel markers utilizing plasma, sputum, and airway samples.
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Affiliation(s)
- Ju Ae Park
- Department of General Surgery, Inova Fairfax Medical Campus, 3300 Gallows Road, Falls Church, VA 22042, USA
| | - Kei Suzuki
- Inova Thoracic Surgery, Schar Cancer Institute, 8081 Innovation Park Drive, Fairfax, VA 22031, USA.
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3
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Amiri P, Ahmadian L, Khajouei R. The applications and the effectiveness of mHealth interventions to manage lung cancer patients: a systematic review. HEALTH AND TECHNOLOGY 2023. [DOI: 10.1007/s12553-023-00735-5] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/11/2023]
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4
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U N R, M A G. BCDU-Net and chronological-AVO based ensemble learning for lung nodule segmentation and classification. COMPUTER METHODS IN BIOMECHANICS AND BIOMEDICAL ENGINEERING: IMAGING & VISUALIZATION 2022. [DOI: 10.1080/21681163.2022.2150891] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/24/2022]
Affiliation(s)
- Ranjitha U N
- Department of Computer Science and Engineering, REVA University, Bangalore, India
| | - Gowtham M A
- Department of Electronics and communication Engineering, AdiChunchanagiri Institute of Technology, Chickkamagalur, India
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5
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Mridha MF, Prodeep AR, Hoque ASMM, Islam MR, Lima AA, Kabir MM, Hamid MA, Watanobe Y. A Comprehensive Survey on the Progress, Process, and Challenges of Lung Cancer Detection and Classification. JOURNAL OF HEALTHCARE ENGINEERING 2022; 2022:5905230. [PMID: 36569180 PMCID: PMC9788902 DOI: 10.1155/2022/5905230] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 07/27/2022] [Revised: 10/17/2022] [Accepted: 11/09/2022] [Indexed: 12/23/2022]
Abstract
Lung cancer is the primary reason of cancer deaths worldwide, and the percentage of death rate is increasing step by step. There are chances of recovering from lung cancer by detecting it early. In any case, because the number of radiologists is limited and they have been working overtime, the increase in image data makes it hard for them to evaluate the images accurately. As a result, many researchers have come up with automated ways to predict the growth of cancer cells using medical imaging methods in a quick and accurate way. Previously, a lot of work was done on computer-aided detection (CADe) and computer-aided diagnosis (CADx) in computed tomography (CT) scan, magnetic resonance imaging (MRI), and X-ray with the goal of effective detection and segmentation of pulmonary nodule, as well as classifying nodules as malignant or benign. But still, no complete comprehensive review that includes all aspects of lung cancer has been done. In this paper, every aspect of lung cancer is discussed in detail, including datasets, image preprocessing, segmentation methods, optimal feature extraction and selection methods, evaluation measurement matrices, and classifiers. Finally, the study looks into several lung cancer-related issues with possible solutions.
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Affiliation(s)
- M. F. Mridha
- Department of Computer Science and Engineering, American International University Bangladesh, Dhaka 1229, Bangladesh
| | - Akibur Rahman Prodeep
- Department of Computer Science and Engineering, Bangladesh University of Business and Technology, Dhaka 1216, Bangladesh
| | - A. S. M. Morshedul Hoque
- Department of Computer Science and Engineering, Bangladesh University of Business and Technology, Dhaka 1216, Bangladesh
| | - Md. Rashedul Islam
- Department of Computer Science and Engineering, University of Asia Pacific, Dhaka 1216, Bangladesh
| | - Aklima Akter Lima
- Department of Computer Science and Engineering, Bangladesh University of Business and Technology, Dhaka 1216, Bangladesh
| | - Muhammad Mohsin Kabir
- Department of Computer Science and Engineering, Bangladesh University of Business and Technology, Dhaka 1216, Bangladesh
| | - Md. Abdul Hamid
- Department of Information Technology, King Abdulaziz University, Jeddah 21589, Saudi Arabia
| | - Yutaka Watanobe
- Department of Computer Science and Engineering, University of Aizu, Aizuwakamatsu 965-8580, Japan
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6
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Classification of breast cancer histology images using MSMV-PFENet. Sci Rep 2022; 12:17447. [PMID: 36261463 PMCID: PMC9581896 DOI: 10.1038/s41598-022-22358-y] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/06/2021] [Accepted: 10/13/2022] [Indexed: 01/12/2023] Open
Abstract
Deep learning has been used extensively in histopathological image classification, but people in this field are still exploring new neural network architectures for more effective and efficient cancer diagnosis. Here, we propose multi-scale, multi-view progressive feature encoding network (MSMV-PFENet) for effective classification. With respect to the density of cell nuclei, we selected the regions potentially related to carcinogenesis at multiple scales from each view. The progressive feature encoding network then extracted the global and local features from these regions. A bidirectional long short-term memory analyzed the encoding vectors to get a category score, and finally the majority voting method integrated different views to classify the histopathological images. We tested our method on the breast cancer histology dataset from the ICIAR 2018 grand challenge. The proposed MSMV-PFENet achieved 93.0[Formula: see text] and 94.8[Formula: see text] accuracies at the patch and image levels, respectively. This method can potentially benefit the clinical cancer diagnosis.
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7
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Deep Learning Algorithms for Diagnosis of Lung Cancer: A Systematic Review and Meta-Analysis. Cancers (Basel) 2022; 14:cancers14163856. [PMID: 36010850 PMCID: PMC9405626 DOI: 10.3390/cancers14163856] [Citation(s) in RCA: 11] [Impact Index Per Article: 5.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/18/2022] [Revised: 07/30/2022] [Accepted: 08/04/2022] [Indexed: 12/19/2022] Open
Abstract
We conducted a systematic review and meta-analysis of the diagnostic performance of current deep learning algorithms for the diagnosis of lung cancer. We searched major databases up to June 2022 to include studies that used artificial intelligence to diagnose lung cancer, using the histopathological analysis of true positive cases as a reference. The quality of the included studies was assessed independently by two authors based on the revised Quality Assessment of Diagnostic Accuracy Studies. Six studies were included in the analysis. The pooled sensitivity and specificity were 0.93 (95% CI 0.85−0.98) and 0.68 (95% CI 0.49−0.84), respectively. Despite the significantly high heterogeneity for sensitivity (I2 = 94%, p < 0.01) and specificity (I2 = 99%, p < 0.01), most of it was attributed to the threshold effect. The pooled SROC curve with a bivariate approach yielded an area under the curve (AUC) of 0.90 (95% CI 0.86 to 0.92). The DOR for the studies was 26.7 (95% CI 19.7−36.2) and heterogeneity was 3% (p = 0.40). In this systematic review and meta-analysis, we found that when using the summary point from the SROC, the pooled sensitivity and specificity of DL algorithms for the diagnosis of lung cancer were 93% and 68%, respectively.
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8
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Nair A, Ramanathan S, Sathiadoss P, Jajodia A, Macdonald DB. Dificultades en la implantación de la inteligencia artificial en la práctica radiológica: lo que el radiólogo necesita saber. RADIOLOGIA 2022. [DOI: 10.1016/j.rx.2022.04.005] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/19/2022]
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9
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Nair A, Ramanathan S, Sathiadoss P, Jajodia A, Blair Macdonald D. Barriers to artificial intelligence implementation in radiology practice: What the radiologist needs to know. RADIOLOGIA 2022; 64:324-332. [DOI: 10.1016/j.rxeng.2022.04.001] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/06/2021] [Accepted: 04/08/2022] [Indexed: 11/16/2022]
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10
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Amin J, Anjum MA, Sharif M, Kadry S, Nadeem A, Ahmad SF. Liver Tumor Localization Based on YOLOv3 and 3D-Semantic Segmentation Using Deep Neural Networks. Diagnostics (Basel) 2022; 12:diagnostics12040823. [PMID: 35453870 PMCID: PMC9025116 DOI: 10.3390/diagnostics12040823] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/02/2022] [Revised: 03/18/2022] [Accepted: 03/22/2022] [Indexed: 12/17/2022] Open
Abstract
Worldwide, more than 1.5 million deaths are occur due to liver cancer every year. The use of computed tomography (CT) for early detection of liver cancer could save millions of lives per year. There is also an urgent need for a computerized method to interpret, detect and analyze CT scans reliably, easily, and correctly. However, precise segmentation of minute tumors is a difficult task because of variation in the shape, intensity, size, low contrast of the tumor, and the adjacent tissues of the liver. To address these concerns, a model comprised of three parts: synthetic image generation, localization, and segmentation, is proposed. An optimized generative adversarial network (GAN) is utilized for generation of synthetic images. The generated images are localized by using the improved localization model, in which deep features are extracted from pre-trained Resnet-50 models and fed into a YOLOv3 detector as an input. The proposed modified model localizes and classifies the minute liver tumor with 0.99 mean average precision (mAp). The third part is segmentation, in which pre-trained Inceptionresnetv2 employed as a base-Network of Deeplabv3 and subsequently is trained on fine-tuned parameters with annotated ground masks. The experiments reflect that the proposed approach has achieved greater than 95% accuracy in the testing phase and it is proven that, in comparison to the recently published work in this domain, this research has localized and segmented the liver and minute liver tumor with more accuracy.
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Affiliation(s)
- Javaria Amin
- Department of Computer Science, University of Wah, Wah Cantt 47040, Pakistan;
| | | | - Muhammad Sharif
- Department of Computer Science, Comsats University Islamabad, Wah Campus, Wah Cantt 47040, Pakistan;
| | - Seifedine Kadry
- Department of Applied Data Science, Noroff University College, 4609 Kristiansand, Norway
- Correspondence:
| | - Ahmed Nadeem
- Department of Pharmacology & Toxicology, College of Pharmacy, King Saud University, P.O. Box 2455, Riyadh 11451, Saudi Arabia; (A.N.); (S.F.A.)
| | - Sheikh F. Ahmad
- Department of Pharmacology & Toxicology, College of Pharmacy, King Saud University, P.O. Box 2455, Riyadh 11451, Saudi Arabia; (A.N.); (S.F.A.)
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11
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Silva F, Pereira T, Neves I, Morgado J, Freitas C, Malafaia M, Sousa J, Fonseca J, Negrão E, Flor de Lima B, Correia da Silva M, Madureira AJ, Ramos I, Costa JL, Hespanhol V, Cunha A, Oliveira HP. Towards Machine Learning-Aided Lung Cancer Clinical Routines: Approaches and Open Challenges. J Pers Med 2022; 12:480. [PMID: 35330479 PMCID: PMC8950137 DOI: 10.3390/jpm12030480] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/07/2022] [Revised: 02/28/2022] [Accepted: 03/10/2022] [Indexed: 12/15/2022] Open
Abstract
Advancements in the development of computer-aided decision (CAD) systems for clinical routines provide unquestionable benefits in connecting human medical expertise with machine intelligence, to achieve better quality healthcare. Considering the large number of incidences and mortality numbers associated with lung cancer, there is a need for the most accurate clinical procedures; thus, the possibility of using artificial intelligence (AI) tools for decision support is becoming a closer reality. At any stage of the lung cancer clinical pathway, specific obstacles are identified and "motivate" the application of innovative AI solutions. This work provides a comprehensive review of the most recent research dedicated toward the development of CAD tools using computed tomography images for lung cancer-related tasks. We discuss the major challenges and provide critical perspectives on future directions. Although we focus on lung cancer in this review, we also provide a more clear definition of the path used to integrate AI in healthcare, emphasizing fundamental research points that are crucial for overcoming current barriers.
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Affiliation(s)
- Francisco Silva
- INESC TEC—Institute for Systems and Computer Engineering, Technology and Science, 4200-465 Porto, Portugal; (I.N.); (J.M.); (M.M.); (J.S.); (J.F.); (A.C.); (H.P.O.)
- FCUP—Faculty of Science, University of Porto, 4169-007 Porto, Portugal
| | - Tania Pereira
- INESC TEC—Institute for Systems and Computer Engineering, Technology and Science, 4200-465 Porto, Portugal; (I.N.); (J.M.); (M.M.); (J.S.); (J.F.); (A.C.); (H.P.O.)
| | - Inês Neves
- INESC TEC—Institute for Systems and Computer Engineering, Technology and Science, 4200-465 Porto, Portugal; (I.N.); (J.M.); (M.M.); (J.S.); (J.F.); (A.C.); (H.P.O.)
- ICBAS—Abel Salazar Biomedical Sciences Institute, University of Porto, 4050-313 Porto, Portugal
| | - Joana Morgado
- INESC TEC—Institute for Systems and Computer Engineering, Technology and Science, 4200-465 Porto, Portugal; (I.N.); (J.M.); (M.M.); (J.S.); (J.F.); (A.C.); (H.P.O.)
| | - Cláudia Freitas
- CHUSJ—Centro Hospitalar e Universitário de São João, 4200-319 Porto, Portugal; (C.F.); (E.N.); (B.F.d.L.); (M.C.d.S.); (A.J.M.); (I.R.); (V.H.)
- FMUP—Faculty of Medicine, University of Porto, 4200-319 Porto, Portugal;
| | - Mafalda Malafaia
- INESC TEC—Institute for Systems and Computer Engineering, Technology and Science, 4200-465 Porto, Portugal; (I.N.); (J.M.); (M.M.); (J.S.); (J.F.); (A.C.); (H.P.O.)
- FEUP—Faculty of Engineering, University of Porto, 4200-465 Porto, Portugal
| | - Joana Sousa
- INESC TEC—Institute for Systems and Computer Engineering, Technology and Science, 4200-465 Porto, Portugal; (I.N.); (J.M.); (M.M.); (J.S.); (J.F.); (A.C.); (H.P.O.)
| | - João Fonseca
- INESC TEC—Institute for Systems and Computer Engineering, Technology and Science, 4200-465 Porto, Portugal; (I.N.); (J.M.); (M.M.); (J.S.); (J.F.); (A.C.); (H.P.O.)
- FEUP—Faculty of Engineering, University of Porto, 4200-465 Porto, Portugal
| | - Eduardo Negrão
- CHUSJ—Centro Hospitalar e Universitário de São João, 4200-319 Porto, Portugal; (C.F.); (E.N.); (B.F.d.L.); (M.C.d.S.); (A.J.M.); (I.R.); (V.H.)
| | - Beatriz Flor de Lima
- CHUSJ—Centro Hospitalar e Universitário de São João, 4200-319 Porto, Portugal; (C.F.); (E.N.); (B.F.d.L.); (M.C.d.S.); (A.J.M.); (I.R.); (V.H.)
| | - Miguel Correia da Silva
- CHUSJ—Centro Hospitalar e Universitário de São João, 4200-319 Porto, Portugal; (C.F.); (E.N.); (B.F.d.L.); (M.C.d.S.); (A.J.M.); (I.R.); (V.H.)
| | - António J. Madureira
- CHUSJ—Centro Hospitalar e Universitário de São João, 4200-319 Porto, Portugal; (C.F.); (E.N.); (B.F.d.L.); (M.C.d.S.); (A.J.M.); (I.R.); (V.H.)
- FMUP—Faculty of Medicine, University of Porto, 4200-319 Porto, Portugal;
| | - Isabel Ramos
- CHUSJ—Centro Hospitalar e Universitário de São João, 4200-319 Porto, Portugal; (C.F.); (E.N.); (B.F.d.L.); (M.C.d.S.); (A.J.M.); (I.R.); (V.H.)
- FMUP—Faculty of Medicine, University of Porto, 4200-319 Porto, Portugal;
| | - José Luis Costa
- FMUP—Faculty of Medicine, University of Porto, 4200-319 Porto, Portugal;
- i3S—Instituto de Investigação e Inovação em Saúde, Universidade do Porto, 4200-135 Porto, Portugal
- IPATIMUP—Institute of Molecular Pathology and Immunology of the University of Porto, 4200-135 Porto, Portugal
| | - Venceslau Hespanhol
- CHUSJ—Centro Hospitalar e Universitário de São João, 4200-319 Porto, Portugal; (C.F.); (E.N.); (B.F.d.L.); (M.C.d.S.); (A.J.M.); (I.R.); (V.H.)
- FMUP—Faculty of Medicine, University of Porto, 4200-319 Porto, Portugal;
| | - António Cunha
- INESC TEC—Institute for Systems and Computer Engineering, Technology and Science, 4200-465 Porto, Portugal; (I.N.); (J.M.); (M.M.); (J.S.); (J.F.); (A.C.); (H.P.O.)
- UTAD—University of Trás-os-Montes and Alto Douro, 5001-801 Vila Real, Portugal
| | - Hélder P. Oliveira
- INESC TEC—Institute for Systems and Computer Engineering, Technology and Science, 4200-465 Porto, Portugal; (I.N.); (J.M.); (M.M.); (J.S.); (J.F.); (A.C.); (H.P.O.)
- FCUP—Faculty of Science, University of Porto, 4169-007 Porto, Portugal
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12
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An ISHAP-based interpretation-model-guided classification method for malignant pulmonary nodule. Knowl Based Syst 2022. [DOI: 10.1016/j.knosys.2021.107778] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/17/2022]
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13
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Jena SR, George ST, Ponraj DN. Lung cancer detection and classification with DGMM-RBCNN technique. Neural Comput Appl 2021. [DOI: 10.1007/s00521-021-06182-5] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/21/2022]
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14
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Kumar Y, Gupta S, Singla R, Hu YC. A Systematic Review of Artificial Intelligence Techniques in Cancer Prediction and Diagnosis. ARCHIVES OF COMPUTATIONAL METHODS IN ENGINEERING : STATE OF THE ART REVIEWS 2021; 29:2043-2070. [PMID: 34602811 PMCID: PMC8475374 DOI: 10.1007/s11831-021-09648-w] [Citation(s) in RCA: 28] [Impact Index Per Article: 9.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/23/2021] [Accepted: 09/11/2021] [Indexed: 05/05/2023]
Abstract
Artificial intelligence has aided in the advancement of healthcare research. The availability of open-source healthcare statistics has prompted researchers to create applications that aid cancer detection and prognosis. Deep learning and machine learning models provide a reliable, rapid, and effective solution to deal with such challenging diseases in these circumstances. PRISMA guidelines had been used to select the articles published on the web of science, EBSCO, and EMBASE between 2009 and 2021. In this study, we performed an efficient search and included the research articles that employed AI-based learning approaches for cancer prediction. A total of 185 papers are considered impactful for cancer prediction using conventional machine and deep learning-based classifications. In addition, the survey also deliberated the work done by the different researchers and highlighted the limitations of the existing literature, and performed the comparison using various parameters such as prediction rate, accuracy, sensitivity, specificity, dice score, detection rate, area undercover, precision, recall, and F1-score. Five investigations have been designed, and solutions to those were explored. Although multiple techniques recommended in the literature have achieved great prediction results, still cancer mortality has not been reduced. Thus, more extensive research to deal with the challenges in the area of cancer prediction is required.
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Affiliation(s)
- Yogesh Kumar
- Department of Computer Engineering, Indus Institute of Technology & Engineering, Indus University, Rancharda, Via: Shilaj, Ahmedabad, Gujarat 382115 India
| | - Surbhi Gupta
- School of Computer Science and Engineering, Model Institute of Engineering and Technology, Kot bhalwal, Jammu, J&K 181122 India
| | - Ruchi Singla
- Department of Research, Innovations, Sponsored Projects and Entrepreneurship, Chandigarh Group of Colleges, Landran, Mohali India
| | - Yu-Chen Hu
- Department of Computer Science and Information Management, Providence University, Taichung City, Taiwan, ROC
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15
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Sharafeldeen A, Elsharkawy M, Alghamdi NS, Soliman A, El-Baz A. Precise Segmentation of COVID-19 Infected Lung from CT Images Based on Adaptive First-Order Appearance Model with Morphological/Anatomical Constraints. SENSORS (BASEL, SWITZERLAND) 2021; 21:5482. [PMID: 34450923 PMCID: PMC8399192 DOI: 10.3390/s21165482] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 06/11/2021] [Revised: 08/08/2021] [Accepted: 08/10/2021] [Indexed: 12/16/2022]
Abstract
A new segmentation technique is introduced for delineating the lung region in 3D computed tomography (CT) images. To accurately model the distribution of Hounsfield scale values within both chest and lung regions, a new probabilistic model is developed that depends on a linear combination of Gaussian (LCG). Moreover, we modified the conventional expectation-maximization (EM) algorithm to be run in a sequential way to estimate both the dominant Gaussian components (one for the lung region and one for the chest region) and the subdominant Gaussian components, which are used to refine the final estimated joint density. To estimate the marginal density from the mixed density, a modified k-means clustering approach is employed to classify the Gaussian subdominant components to determine which components belong properly to a lung and which components belong to a chest. The initial segmentation, based on the LCG-model, is then refined by the imposition of 3D morphological constraints based on a 3D Markov-Gibbs random field (MGRF) with analytically estimated potentials. The proposed approach was tested on CT data from 32 coronavirus disease 2019 (COVID-19) patients. Segmentation quality was quantitatively evaluated using four metrics: Dice similarity coefficient (DSC), overlap coefficient, 95th-percentile bidirectional Hausdorff distance (BHD), and absolute lung volume difference (ALVD), and it achieved 95.67±1.83%, 91.76±3.29%, 4.86±5.01, and 2.93±2.39, respectively. The reported results showed the capability of the proposed approach to accurately segment healthy lung tissues in addition to pathological lung tissues caused by COVID-19, outperforming four current, state-of-the-art deep learning-based lung segmentation approaches.
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Affiliation(s)
- Ahmed Sharafeldeen
- BioImaging Laboratory, Department of Bioengineering, University of Louisville, Louisville, KY 40292, USA; (A.S.); (M.E.); (A.S.)
| | - Mohamed Elsharkawy
- BioImaging Laboratory, Department of Bioengineering, University of Louisville, Louisville, KY 40292, USA; (A.S.); (M.E.); (A.S.)
| | - Norah Saleh Alghamdi
- College of Computer and Information Science, Princess Nourah Bint Abdulrahman University, Riyadh 11564, Saudi Arabia
| | - Ahmed Soliman
- BioImaging Laboratory, Department of Bioengineering, University of Louisville, Louisville, KY 40292, USA; (A.S.); (M.E.); (A.S.)
| | - Ayman El-Baz
- BioImaging Laboratory, Department of Bioengineering, University of Louisville, Louisville, KY 40292, USA; (A.S.); (M.E.); (A.S.)
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16
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Machine Learning and Feature Selection Methods for EGFR Mutation Status Prediction in Lung Cancer. APPLIED SCIENCES-BASEL 2021. [DOI: 10.3390/app11073273] [Citation(s) in RCA: 14] [Impact Index Per Article: 4.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 02/07/2023]
Abstract
The evolution of personalized medicine has changed the therapeutic strategy from classical chemotherapy and radiotherapy to a genetic modification targeted therapy, and although biopsy is the traditional method to genetically characterize lung cancer tumor, it is an invasive and painful procedure for the patient. Nodule image features extracted from computed tomography (CT) scans have been used to create machine learning models that predict gene mutation status in a noninvasive, fast, and easy-to-use manner. However, recent studies have shown that radiomic features extracted from an extended region of interest (ROI) beyond the tumor, might be more relevant to predict the mutation status in lung cancer, and consequently may be used to significantly decrease the mortality rate of patients battling this condition. In this work, we investigated the relation between image phenotypes and the mutation status of Epidermal Growth Factor Receptor (EGFR), the most frequently mutated gene in lung cancer with several approved targeted-therapies, using radiomic features extracted from the lung containing the nodule. A variety of linear, nonlinear, and ensemble predictive classification models, along with several feature selection methods, were used to classify the binary outcome of wild-type or mutant EGFR mutation status. The results show that a comprehensive approach using a ROI that included the lung with nodule can capture relevant information and successfully predict the EGFR mutation status with increased performance compared to local nodule analyses. Linear Support Vector Machine, Elastic Net, and Logistic Regression, combined with the Principal Component Analysis feature selection method implemented with 70% of variance in the feature set, were the best-performing classifiers, reaching Area Under the Curve (AUC) values ranging from 0.725 to 0.737. This approach that exploits a holistic analysis indicates that information from more extensive regions of the lung containing the nodule allows a more complete lung cancer characterization and should be considered in future radiogenomic studies.
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Pereira T, Freitas C, Costa JL, Morgado J, Silva F, Negrão E, de Lima BF, da Silva MC, Madureira AJ, Ramos I, Hespanhol V, Cunha A, Oliveira HP. Comprehensive Perspective for Lung Cancer Characterisation Based on AI Solutions Using CT Images. J Clin Med 2020; 10:E118. [PMID: 33396348 PMCID: PMC7796087 DOI: 10.3390/jcm10010118] [Citation(s) in RCA: 9] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/22/2020] [Revised: 12/28/2020] [Accepted: 12/28/2020] [Indexed: 12/16/2022] Open
Abstract
Lung cancer is still the leading cause of cancer death in the world. For this reason, novel approaches for early and more accurate diagnosis are needed. Computer-aided decision (CAD) can be an interesting option for a noninvasive tumour characterisation based on thoracic computed tomography (CT) image analysis. Until now, radiomics have been focused on tumour features analysis, and have not considered the information on other lung structures that can have relevant features for tumour genotype classification, especially for epidermal growth factor receptor (EGFR), which is the mutation with the most successful targeted therapies. With this perspective paper, we aim to explore a comprehensive analysis of the need to combine the information from tumours with other lung structures for the next generation of CADs, which could create a high impact on targeted therapies and personalised medicine. The forthcoming artificial intelligence (AI)-based approaches for lung cancer assessment should be able to make a holistic analysis, capturing information from pathological processes involved in cancer development. The powerful and interpretable AI models allow us to identify novel biomarkers of cancer development, contributing to new insights about the pathological processes, and making a more accurate diagnosis to help in the treatment plan selection.
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Affiliation(s)
- Tania Pereira
- Institute for Systems and Computer Engineering, Technology and Science, INESC TEC, 4200-465 Porto, Portugal; (J.M.); (F.S.); (A.C.); (H.P.O.)
| | - Cláudia Freitas
- Centro Hospitalar e Universitário de São João, CHUSJ, 4200-319 Porto, Portugal; (C.F.); (E.N.); (B.F.d.L.); (M.C.d.S.); (A.J.M.); (I.R.); (V.H.)
- Faculty of Medicine, University of Porto, FMUP, 4200-319 Porto, Portugal;
| | - José Luis Costa
- Faculty of Medicine, University of Porto, FMUP, 4200-319 Porto, Portugal;
- Institute for Research and Innovation in Health of the University of Porto, i3S, 4200-135 Porto, Portugal
- Institute of Molecular Pathology and Immunology of the University of Porto, IPATIMUP, 4200-135 Porto, Portugal
| | - Joana Morgado
- Institute for Systems and Computer Engineering, Technology and Science, INESC TEC, 4200-465 Porto, Portugal; (J.M.); (F.S.); (A.C.); (H.P.O.)
- Faculty of Science, University of Porto, FCUP, 4169-007 Porto, Portugal
| | - Francisco Silva
- Institute for Systems and Computer Engineering, Technology and Science, INESC TEC, 4200-465 Porto, Portugal; (J.M.); (F.S.); (A.C.); (H.P.O.)
| | - Eduardo Negrão
- Centro Hospitalar e Universitário de São João, CHUSJ, 4200-319 Porto, Portugal; (C.F.); (E.N.); (B.F.d.L.); (M.C.d.S.); (A.J.M.); (I.R.); (V.H.)
| | - Beatriz Flor de Lima
- Centro Hospitalar e Universitário de São João, CHUSJ, 4200-319 Porto, Portugal; (C.F.); (E.N.); (B.F.d.L.); (M.C.d.S.); (A.J.M.); (I.R.); (V.H.)
| | - Miguel Correia da Silva
- Centro Hospitalar e Universitário de São João, CHUSJ, 4200-319 Porto, Portugal; (C.F.); (E.N.); (B.F.d.L.); (M.C.d.S.); (A.J.M.); (I.R.); (V.H.)
| | - António J. Madureira
- Centro Hospitalar e Universitário de São João, CHUSJ, 4200-319 Porto, Portugal; (C.F.); (E.N.); (B.F.d.L.); (M.C.d.S.); (A.J.M.); (I.R.); (V.H.)
| | - Isabel Ramos
- Centro Hospitalar e Universitário de São João, CHUSJ, 4200-319 Porto, Portugal; (C.F.); (E.N.); (B.F.d.L.); (M.C.d.S.); (A.J.M.); (I.R.); (V.H.)
- Faculty of Medicine, University of Porto, FMUP, 4200-319 Porto, Portugal;
| | - Venceslau Hespanhol
- Centro Hospitalar e Universitário de São João, CHUSJ, 4200-319 Porto, Portugal; (C.F.); (E.N.); (B.F.d.L.); (M.C.d.S.); (A.J.M.); (I.R.); (V.H.)
- Faculty of Medicine, University of Porto, FMUP, 4200-319 Porto, Portugal;
| | - António Cunha
- Institute for Systems and Computer Engineering, Technology and Science, INESC TEC, 4200-465 Porto, Portugal; (J.M.); (F.S.); (A.C.); (H.P.O.)
- Department of Engineering, University of Trás-os-Montes and Alto Douro, UTAD, 5001-801 Vila Real, Portugal
| | - Hélder P. Oliveira
- Institute for Systems and Computer Engineering, Technology and Science, INESC TEC, 4200-465 Porto, Portugal; (J.M.); (F.S.); (A.C.); (H.P.O.)
- Faculty of Science, University of Porto, FCUP, 4169-007 Porto, Portugal
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Shakir H, Rasheed H, Rasool Khan TM. Radiomic feature selection for lung cancer classifiers. JOURNAL OF INTELLIGENT & FUZZY SYSTEMS 2020. [DOI: 10.3233/jifs-179672] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/15/2022]
Affiliation(s)
- Hina Shakir
- Department of Electrical Engineering, Bahria University, Karachi, Pakistan
| | - Haroon Rasheed
- Department of Electrical Engineering, Bahria University, Karachi, Pakistan
| | - Tariq Mairaj Rasool Khan
- Department of Electrical and Power Engineering, PNEC, National University of Science and Technology, Pakistan
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Recent developments and advances in secondary prevention of lung cancer. Eur J Cancer Prev 2020; 29:321-328. [PMID: 32452945 DOI: 10.1097/cej.0000000000000586] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/27/2022]
Abstract
Lung cancer prevention may include primary prevention strategies, such as corrections of working conditions and life style - primarily smoking cessation - as well as secondary prevention strategies, aiming at early detection that allows better survival rates and limited resections. This review summarizes recent developments and advances in secondary prevention, focusing on recent technological tools for an effective early diagnosis.
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A Two-Stage Framework for Automated Malignant Pulmonary Nodule Detection in CT Scans. Diagnostics (Basel) 2020; 10:diagnostics10030131. [PMID: 32121281 PMCID: PMC7151085 DOI: 10.3390/diagnostics10030131] [Citation(s) in RCA: 16] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/07/2020] [Revised: 02/13/2020] [Accepted: 02/18/2020] [Indexed: 11/17/2022] Open
Abstract
This research is concerned with malignant pulmonary nodule detection (PND) in low-dose CT scans. Due to its crucial role in the early diagnosis of lung cancer, PND has considerable potential in improving the survival rate of patients. We propose a two-stage framework that exploits the ever-growing advances in deep neural network models, and that is comprised of a semantic segmentation stage followed by localization and classification. We employ the recently published DeepLab model for semantic segmentation, and we show that it significantly improves the accuracy of nodule detection compared to the classical U-Net model and its most recent variants. Using the widely adopted Lung Nodule Analysis dataset (LUNA16), we evaluate the performance of the semantic segmentation stage by adopting two network backbones, namely, MobileNet-V2 and Xception. We present the impact of various model training parameters and the computational time on the detection accuracy, featuring a 79.1% mean intersection-over-union (mIoU) and an 88.34% dice coefficient. This represents a mIoU increase of 60% and a dice coefficient increase of 30% compared to U-Net. The second stage involves feeding the output of the DeepLab-based semantic segmentation to a localization-then-classification stage. The second stage is realized using Faster RCNN and SSD, with an Inception-V2 as a backbone. On LUNA16, the two-stage framework attained a sensitivity of 96.4%, outperforming other recent models in the literature, including deep models. Finally, we show that adopting a transfer learning approach, particularly, the DeepLab model weights of the first stage of the framework, to infer binary (malignant-benign) labels on the Kaggle dataset for pulmonary nodules achieves a classification accuracy of 95.66%, which represents approximately 4% improvement over the recent literature.
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21
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Huang P, Lin CT, Li Y, Tammemagi MC, Brock MV, Atkar-Khattra S, Xu Y, Hu P, Mayo JR, Schmidt H, Gingras M, Pasian S, Stewart L, Tsai S, Seely JM, Manos D, Burrowes P, Bhatia R, Tsao MS, Lam S. Prediction of lung cancer risk at follow-up screening with low-dose CT: a training and validation study of a deep learning method. LANCET DIGITAL HEALTH 2019; 1:e353-e362. [PMID: 32864596 DOI: 10.1016/s2589-7500(19)30159-1] [Citation(s) in RCA: 61] [Impact Index Per Article: 12.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/16/2022]
Abstract
Background Current lung cancer screening guidelines use mean diameter, volume or density of the largest lung nodule in the prior computed tomography (CT) or appearance of new nodule to determine the timing of the next CT. We aimed at developing a more accurate screening protocol by estimating the 3-year lung cancer risk after two screening CTs using deep machine learning (ML) of radiologist CT reading and other universally available clinical information. Methods A deep machine learning (ML) algorithm was developed from 25,097 participants who had received at least two CT screenings up to two years apart in the National Lung Screening Trial. Double-blinded validation was performed using 2,294 participants from the Pan-Canadian Early Detection of Lung Cancer Study (PanCan). Performance of ML score to inform lung cancer incidence was compared with Lung-RADS and volume doubling time using time-dependent ROC analysis. Exploratory analysis was performed to identify individuals with aggressive cancers and higher mortality rates. Findings In the PanCan validation cohort, ML showed excellent discrimination with a 1-, 2- and 3-year time-dependent AUC values for cancer diagnosis of 0·968±0·013, 0·946±0·013 and 0·899±0·017. Although high ML score cohort included only 10% of the PanCan sample, it identified 94%, 85%, and 71% of incident and interval lung cancers diagnosed within 1, 2, and 3 years, respectively, after the second screening CT. Furthermore, individuals with high ML score had significantly higher mortality rates (HR=16·07, p<0·001) compared to those with lower risk. Interpretation ML tool that recognizes patterns in both temporal and spatial changes as well as synergy among changes in nodule and non-nodule features may be used to accurately guide clinical management after the next scheduled repeat screening CT.
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Affiliation(s)
- Peng Huang
- Department of Oncology, Johns Hopkins University, Baltimore, Maryland, USA.,Department of Biostatistics, Johns Hopkins University, Baltimore, Maryland, USA.,Co-first authors
| | - Cheng T Lin
- Department of Radiology, Johns Hopkins University, Baltimore, Maryland, USA.,Co-first authors
| | - Yuliang Li
- Department of Applied Mathematics & Statistics, Johns Hopkins University, Baltimore, Maryland, USA
| | - Martin C Tammemagi
- Department of Community Health Sciences, Brock University, St. Catharines, Ontario, Canada
| | - Malcolm V Brock
- Department of Surgery, Johns Hopkins University, Baltimore, Maryland, USA
| | | | - Yanxun Xu
- Department of Applied Mathematics & Statistics, Johns Hopkins University, Baltimore, Maryland, USA
| | - Ping Hu
- Division of Cancer Prevention, National Cancer Institute, Canada
| | - John R Mayo
- University of British Columbia and Vancouver General Hospital, Vancouver, British Columbia, Canada
| | - Heidi Schmidt
- University Health Network-Princess Margaret Cancer Centre and Toronto General Hospital, Toronto, Ontario, Canada
| | - Michel Gingras
- Institut universitaire de cardiologie et, de pneumologie de Québec, Canada
| | - Sergio Pasian
- Institut universitaire de cardiologie et, de pneumologie de Québec, Canada
| | - Lori Stewart
- Department of Diagnostic Imaging, Juravinski Hospital, Hamilton, Ontario, Canada
| | - Scott Tsai
- Department of Diagnostic Imaging, Juravinski Hospital, Hamilton, Ontario, Canada
| | - Jean M Seely
- Ottawa Hospital Research Institute and the University of Ottawa, Ottawa, Ontario, Canada
| | - Daria Manos
- Dalhousie University, Halifax, Nova Scotia, Canada
| | - Paul Burrowes
- University of Calgary, Foothills Medical Centre, Calgary, Alberta, Canada
| | | | - Ming-Sound Tsao
- University Health Network-Princess Margaret Cancer Centre and Toronto General Hospital, Toronto, Ontario, Canada
| | - Stephen Lam
- University of British Columbia-British Columbia Cancer Agency and Vancouver General Hospital, Vancouver, British Columbia, Canada
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Shaukat F, Raja G, Frangi AF. Computer-aided detection of lung nodules: a review. J Med Imaging (Bellingham) 2019. [DOI: 10.1117/1.jmi.6.2.020901] [Citation(s) in RCA: 20] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/14/2022] Open
Affiliation(s)
- Furqan Shaukat
- University of Engineering and Technology, Department of Electrical Engineering, Taxila
| | - Gulistan Raja
- University of Engineering and Technology, Department of Electrical Engineering, Taxila
| | - Alejandro F. Frangi
- University of Leeds Woodhouse Lane, School of Computing and School of Medicine, Leeds
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Jin L, Sun Y, Li M. Use of an Anthropomorphic Chest Model to Evaluate Multiple Scanning Protocols for High-Definition and Standard-Definition Computed Tomography to Detect Small Pulmonary Nodules. Med Sci Monit 2019; 25:2195-2205. [PMID: 30907379 PMCID: PMC6442497 DOI: 10.12659/msm.913243] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/16/2022] Open
Abstract
BACKGROUND This study aimed to use the LUNGMAN N1 anthropomorphic chest model to evaluate protocols for high-definition computed tomography (HDCT) and standard-definition CT (SDCT) to detect and compare small pulmonary nodules and determine the most appropriate low-dose scanning protocols. MATERIAL AND METHODS HDCT imaging used the Discovery HD750 scanner (80, 100, 120 and 140 kVp; 360, 320, 280, 240, 200, 160, 120, 80, 40, and 20 mA), and SDCT imaging used the Lightspeed VCT scanner (80, 120, and 140 kVp; 360, 320, 280, 240, 200, 160, 120, 80, 40, and 20 mA). The LUNGMAN N1 anthropomorphic chest model contained artificial pulmonary nodules (diameter: 5, 8, 10, and 12 mm). Low-dose scanning protocols were used in image acquisition. Two experienced radiologists evaluated the image quality. The combinations of voltage, tube current, image noise, and radiation dose were recorded. Consistency of the image quality between raters was assessed by kappa statistical analysis. RESULTS Seventy CT scans of pulmonary nodules (diameter, 5-12 mm) were performed. There was a high degree of consistency for image quality between the two observers (K=0.929 for 5 mm nodules; K=0.819 for overall image quality). For 8 mm nodules, 100% were detected on both SDCT and HDCT. HDCT outperformed SDCT by 5%, in terms of effective dose. There was no significant difference in image quality between the SDCT and HDCT scanners. CONCLUSIONS Using an anthropomorphic chest model, the identification and image quality using SDCT was similar to that of HDCT for small pulmonary nodules between 5-12 mm.
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Affiliation(s)
- Liang Jin
- Department of Radiology, Huadong Hospital, Affiliated to Fudan University, Shanghai, China (mainland)
| | - Yingli Sun
- Department of Radiology, Huadong Hospital, Affiliated to Fudan University, Shanghai, China (mainland)
| | - Ming Li
- Department of Radiology, Huadong Hospital, Affiliated to Fudan University, Shanghai, China (mainland)
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The Added Value of Computer-aided Detection of Small Pulmonary Nodules and Missed Lung Cancers. J Thorac Imaging 2019; 33:390-395. [PMID: 30239461 DOI: 10.1097/rti.0000000000000362] [Citation(s) in RCA: 14] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/17/2022]
Abstract
Lung cancer at its earliest stage is typically manifested on computed tomography as a pulmonary nodule, which could be detected by low-dose multidetector computed tomography technology and the use of thinner collimation. Within the last 2 decades, computer-aided detection (CAD) of pulmonary nodules has been developed to meet the increasing demand for lung cancer screening computed tomography with a larger set of images per scan. This review introduced the basic techniques and then summarized the up-to-date applications of CAD systems in clinical and research programs and in the low-dose lung cancer screening trials, especially in the detection of small pulmonary nodules and missed lung cancers. Many studies have already shown that the CAD systems could increase the sensitivity and reduce the false-positive rate in the diagnosis of pulmonary nodules, especially for the small and isolated nodules. Further improvements to the current CAD schemes are needed to detect nodules accurately, particularly for subsolid nodules.
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Lung Cancer Screening, Towards a Multidimensional Approach: Why and How? Cancers (Basel) 2019; 11:cancers11020212. [PMID: 30759893 PMCID: PMC6406662 DOI: 10.3390/cancers11020212] [Citation(s) in RCA: 19] [Impact Index Per Article: 3.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/10/2019] [Revised: 02/06/2019] [Accepted: 02/06/2019] [Indexed: 12/19/2022] Open
Abstract
Early-stage treatment improves prognosis of lung cancer and two large randomized controlled trials have shown that early detection with low-dose computed tomography (LDCT) reduces mortality. Despite this, lung cancer screening (LCS) remains challenging. In the context of a global shortage of radiologists, the high rate of false-positive LDCT results in overloading of existing lung cancer clinics and multidisciplinary teams. Thus, to provide patients with earlier access to life-saving surgical interventions, there is an urgent need to improve LDCT-based LCS and especially to reduce the false-positive rate that plagues the current detection technology. In this context, LCS can be improved in three ways: (1) by refining selection criteria (risk factor assessment), (2) by using Computer Aided Diagnosis (CAD) to make it easier to interpret chest CTs, and (3) by using biological blood signatures for early cancer detection, to both spot the optimal target population and help classify lung nodules. These three main ways of improving LCS are discussed in this review.
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Jung H, Kim B, Lee I, Lee J, Kang J. Classification of lung nodules in CT scans using three-dimensional deep convolutional neural networks with a checkpoint ensemble method. BMC Med Imaging 2018; 18:48. [PMID: 30509191 PMCID: PMC6276244 DOI: 10.1186/s12880-018-0286-0] [Citation(s) in RCA: 33] [Impact Index Per Article: 5.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/03/2018] [Accepted: 10/24/2018] [Indexed: 01/28/2023] Open
Abstract
BACKGROUND Accurately detecting and examining lung nodules early is key in diagnosing lung cancers and thus one of the best ways to prevent lung cancer deaths. Radiologists spend countless hours detecting small spherical-shaped nodules in computed tomography (CT) images. In addition, even after detecting nodule candidates, a considerable amount of effort and time is required for them to determine whether they are real nodules. The aim of this paper is to introduce a high performance nodule classification method that uses three dimensional deep convolutional neural networks (DCNNs) and an ensemble method to distinguish nodules between non-nodules. METHODS In this paper, we use a three dimensional deep convolutional neural network (3D DCNN) with shortcut connections and a 3D DCNN with dense connections for lung nodule classification. The shortcut connections and dense connections successfully alleviate the gradient vanishing problem by allowing the gradient to pass quickly and directly. Connections help deep structured networks to obtain general as well as distinctive features of lung nodules. Moreover, we increased the dimension of DCNNs from two to three to capture 3D features. Compared with shallow 3D CNNs used in previous studies, deep 3D CNNs more effectively capture the features of spherical-shaped nodules. In addition, we use an alternative ensemble method called the checkpoint ensemble method to boost performance. RESULTS The performance of our nodule classification method is compared with that of the state-of-the-art methods which were used in the LUng Nodule Analysis 2016 Challenge. Our method achieves higher competition performance metric (CPM) scores than the state-of-the-art methods using deep learning. In the experimental setup ESB-ALL, the 3D DCNN with shortcut connections and the 3D DCNN with dense connections using the checkpoint ensemble method achieved the highest CPM score of 0.910. CONCLUSION The result demonstrates that our method of using a 3D DCNN with shortcut connections, a 3D DCNN with dense connections, and the checkpoint ensemble method is effective for capturing 3D features of nodules and distinguishing nodules between non-nodules.
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Affiliation(s)
- Hwejin Jung
- Department of Computer Science and Engineering, Korea University, Seoul, Republic of Korea
| | - Bumsoo Kim
- Department of Computer Science and Engineering, Korea University, Seoul, Republic of Korea
| | - Inyeop Lee
- Department of Computer Science and Engineering, Korea University, Seoul, Republic of Korea
| | - Junhyun Lee
- Department of Computer Science and Engineering, Korea University, Seoul, Republic of Korea
| | - Jaewoo Kang
- Department of Computer Science and Engineering, Korea University, Seoul, Republic of Korea
- Interdisciplinary Graduate Program in Bioinformatics, Korea University, Seoul, Republic of Korea
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Abd. Rahni AA, Fazwan Mohamed Fuzaie M, Al Irr OI. Automated Bed Detection and Removal from Abdominal CT Images for Automatic Segmentation Applications. 2018 IEEE-EMBS CONFERENCE ON BIOMEDICAL ENGINEERING AND SCIENCES (IECBES) 2018. [DOI: 10.1109/iecbes.2018.8626638] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 09/02/2023]
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Abstract
Artificial intelligence (AI) algorithms, particularly deep learning, have demonstrated remarkable progress in image-recognition tasks. Methods ranging from convolutional neural networks to variational autoencoders have found myriad applications in the medical image analysis field, propelling it forward at a rapid pace. Historically, in radiology practice, trained physicians visually assessed medical images for the detection, characterization and monitoring of diseases. AI methods excel at automatically recognizing complex patterns in imaging data and providing quantitative, rather than qualitative, assessments of radiographic characteristics. In this Opinion article, we establish a general understanding of AI methods, particularly those pertaining to image-based tasks. We explore how these methods could impact multiple facets of radiology, with a general focus on applications in oncology, and demonstrate ways in which these methods are advancing the field. Finally, we discuss the challenges facing clinical implementation and provide our perspective on how the domain could be advanced.
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Affiliation(s)
- Ahmed Hosny
- Department of Radiation Oncology, Dana-Farber Cancer Institute, Harvard Medical School, Boston, MA, USA
| | - Chintan Parmar
- Department of Radiation Oncology, Dana-Farber Cancer Institute, Harvard Medical School, Boston, MA, USA
| | - John Quackenbush
- Department of Biostatistics & Computational Biology, Dana-Farber Cancer Institute, Boston, MA, USA
- Department of Cancer Biology, Dana-Farber Cancer Institute, Boston, MA, USA
| | - Lawrence H Schwartz
- Department of Radiology, Columbia University College of Physicians and Surgeons, New York, NY, USA
- Department of Radiology, New York Presbyterian Hospital, New York, NY, USA
| | - Hugo J W L Aerts
- Department of Radiation Oncology, Dana-Farber Cancer Institute, Harvard Medical School, Boston, MA, USA.
- Department of Radiology, Dana-Farber Cancer Institute, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA.
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Abstract
Artificial intelligence (AI) algorithms, particularly deep learning, have demonstrated remarkable progress in image-recognition tasks. Methods ranging from convolutional neural networks to variational autoencoders have found myriad applications in the medical image analysis field, propelling it forward at a rapid pace. Historically, in radiology practice, trained physicians visually assessed medical images for the detection, characterization and monitoring of diseases. AI methods excel at automatically recognizing complex patterns in imaging data and providing quantitative, rather than qualitative, assessments of radiographic characteristics. In this Opinion article, we establish a general understanding of AI methods, particularly those pertaining to image-based tasks. We explore how these methods could impact multiple facets of radiology, with a general focus on applications in oncology, and demonstrate ways in which these methods are advancing the field. Finally, we discuss the challenges facing clinical implementation and provide our perspective on how the domain could be advanced.
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Affiliation(s)
- Ahmed Hosny
- Department of Radiation Oncology, Dana-Farber Cancer Institute, Harvard Medical School, Boston, MA, USA
| | - Chintan Parmar
- Department of Radiation Oncology, Dana-Farber Cancer Institute, Harvard Medical School, Boston, MA, USA
| | - John Quackenbush
- Department of Biostatistics & Computational Biology, Dana-Farber Cancer Institute, Boston, MA, USA
- Department of Cancer Biology, Dana-Farber Cancer Institute, Boston, MA, USA
| | - Lawrence H Schwartz
- Department of Radiology, Columbia University College of Physicians and Surgeons, New York, NY, USA
- Department of Radiology, New York Presbyterian Hospital, New York, NY, USA
| | - Hugo J W L Aerts
- Department of Radiation Oncology, Dana-Farber Cancer Institute, Harvard Medical School, Boston, MA, USA.
- Department of Radiology, Dana-Farber Cancer Institute, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA.
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Nishio M, Sugiyama O, Yakami M, Ueno S, Kubo T, Kuroda T, Togashi K. Computer-aided diagnosis of lung nodule classification between benign nodule, primary lung cancer, and metastatic lung cancer at different image size using deep convolutional neural network with transfer learning. PLoS One 2018; 13:e0200721. [PMID: 30052644 PMCID: PMC6063408 DOI: 10.1371/journal.pone.0200721] [Citation(s) in RCA: 86] [Impact Index Per Article: 14.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/11/2018] [Accepted: 05/29/2018] [Indexed: 12/31/2022] Open
Abstract
We developed a computer-aided diagnosis (CADx) method for classification between benign nodule, primary lung cancer, and metastatic lung cancer and evaluated the following: (i) the usefulness of the deep convolutional neural network (DCNN) for CADx of the ternary classification, compared with a conventional method (hand-crafted imaging feature plus machine learning), (ii) the effectiveness of transfer learning, and (iii) the effect of image size as the DCNN input. Among 1240 patients of previously-built database, computed tomography images and clinical information of 1236 patients were included. For the conventional method, CADx was performed by using rotation-invariant uniform-pattern local binary pattern on three orthogonal planes with a support vector machine. For the DCNN method, CADx was evaluated using the VGG-16 convolutional neural network with and without transfer learning, and hyperparameter optimization of the DCNN method was performed by random search. The best averaged validation accuracies of CADx were 55.9%, 68.0%, and 62.4% for the conventional method, the DCNN method with transfer learning, and the DCNN method without transfer learning, respectively. For image size of 56, 112, and 224, the best averaged validation accuracy for the DCNN with transfer learning were 60.7%, 64.7%, and 68.0%, respectively. DCNN was better than the conventional method for CADx, and the accuracy of DCNN improved when using transfer learning. Also, we found that larger image sizes as inputs to DCNN improved the accuracy of lung nodule classification.
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Affiliation(s)
- Mizuho Nishio
- Department of Diagnostic Imaging and Nuclear Medicine, Kyoto University Graduate School of Medicine, Kyoto, Japan
- Preemptive Medicine and Lifestyle-related Disease Research Center, Kyoto University Hospital, Kyoto, Japan
- * E-mail: ,
| | - Osamu Sugiyama
- Preemptive Medicine and Lifestyle-related Disease Research Center, Kyoto University Hospital, Kyoto, Japan
| | - Masahiro Yakami
- Department of Diagnostic Imaging and Nuclear Medicine, Kyoto University Graduate School of Medicine, Kyoto, Japan
- Preemptive Medicine and Lifestyle-related Disease Research Center, Kyoto University Hospital, Kyoto, Japan
| | - Syoko Ueno
- Department of Social Informatics, Kyoto University Graduate School of Informatics Yoshidahonmachi, Kyoto, Japan
| | - Takeshi Kubo
- Department of Diagnostic Imaging and Nuclear Medicine, Kyoto University Graduate School of Medicine, Kyoto, Japan
| | - Tomohiro Kuroda
- Division of Medical Information Technology and Administrative Planning, Kyoto University Hospital, Kyoto, Japan
| | - Kaori Togashi
- Department of Diagnostic Imaging and Nuclear Medicine, Kyoto University Graduate School of Medicine, Kyoto, Japan
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Nishio M, Nishizawa M, Sugiyama O, Kojima R, Yakami M, Kuroda T, Togashi K. Computer-aided diagnosis of lung nodule using gradient tree boosting and Bayesian optimization. PLoS One 2018; 13:e0195875. [PMID: 29672639 PMCID: PMC5908232 DOI: 10.1371/journal.pone.0195875] [Citation(s) in RCA: 46] [Impact Index Per Article: 7.7] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/20/2017] [Accepted: 03/31/2018] [Indexed: 12/23/2022] Open
Abstract
We aimed to evaluate a computer-aided diagnosis (CADx) system for lung nodule classification focussing on (i) usefulness of the conventional CADx system (hand-crafted imaging feature + machine learning algorithm), (ii) comparison between support vector machine (SVM) and gradient tree boosting (XGBoost) as machine learning algorithms, and (iii) effectiveness of parameter optimization using Bayesian optimization and random search. Data on 99 lung nodules (62 lung cancers and 37 benign lung nodules) were included from public databases of CT images. A variant of the local binary pattern was used for calculating a feature vector. SVM or XGBoost was trained using the feature vector and its corresponding label. Tree Parzen Estimator (TPE) was used as Bayesian optimization for parameters of SVM and XGBoost. Random search was done for comparison with TPE. Leave-one-out cross-validation was used for optimizing and evaluating the performance of our CADx system. Performance was evaluated using area under the curve (AUC) of receiver operating characteristic analysis. AUC was calculated 10 times, and its average was obtained. The best averaged AUC of SVM and XGBoost was 0.850 and 0.896, respectively; both were obtained using TPE. XGBoost was generally superior to SVM. Optimal parameters for achieving high AUC were obtained with fewer numbers of trials when using TPE, compared with random search. Bayesian optimization of SVM and XGBoost parameters was more efficient than random search. Based on observer study, AUC values of two board-certified radiologists were 0.898 and 0.822. The results show that diagnostic accuracy of our CADx system was comparable to that of radiologists with respect to classifying lung nodules.
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Affiliation(s)
- Mizuho Nishio
- Department of Diagnostic Imaging and Nuclear Medicine, Kyoto University Graduate School of Medicine, Shogoin, Sakyo-ku, Kyoto, Kyoto, Japan
- Preemptive Medicine and Lifestyle Disease Research Center, Kyoto University Hospital, Shogoin, Sakyo-ku, Kyoto, Kyoto, Japan
- * E-mail: ,
| | - Mitsuo Nishizawa
- Department of Radiology, Osaka Medical College, Takatsuki, Osaka, Japan
| | - Osamu Sugiyama
- Preemptive Medicine and Lifestyle Disease Research Center, Kyoto University Hospital, Shogoin, Sakyo-ku, Kyoto, Kyoto, Japan
| | - Ryosuke Kojima
- Department of Biomedical Data Intelligence, Kyoto University Graduate School of Medicine, Sakyo-ku, Kyoto, Kyoto, Japan
| | - Masahiro Yakami
- Department of Diagnostic Imaging and Nuclear Medicine, Kyoto University Graduate School of Medicine, Shogoin, Sakyo-ku, Kyoto, Kyoto, Japan
- Preemptive Medicine and Lifestyle Disease Research Center, Kyoto University Hospital, Shogoin, Sakyo-ku, Kyoto, Kyoto, Japan
| | - Tomohiro Kuroda
- Division of Medical Information Technology and Administrative Plannnig, Kyoto University Hospital, Shogoin, Sakyo-ku, Kyoto, Kyoto, Japan
| | - Kaori Togashi
- Department of Diagnostic Imaging and Nuclear Medicine, Kyoto University Graduate School of Medicine, Shogoin, Sakyo-ku, Kyoto, Kyoto, Japan
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Burt JR, Torosdagli N, Khosravan N, RaviPrakash H, Mortazi A, Tissavirasingham F, Hussein S, Bagci U. Deep learning beyond cats and dogs: recent advances in diagnosing breast cancer with deep neural networks. Br J Radiol 2018; 91:20170545. [PMID: 29565644 DOI: 10.1259/bjr.20170545] [Citation(s) in RCA: 60] [Impact Index Per Article: 10.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/05/2022] Open
Abstract
Deep learning has demonstrated tremendous revolutionary changes in the computing industry and its effects in radiology and imaging sciences have begun to dramatically change screening paradigms. Specifically, these advances have influenced the development of computer-aided detection and diagnosis (CAD) systems. These technologies have long been thought of as "second-opinion" tools for radiologists and clinicians. However, with significant improvements in deep neural networks, the diagnostic capabilities of learning algorithms are approaching levels of human expertise (radiologists, clinicians etc.), shifting the CAD paradigm from a "second opinion" tool to a more collaborative utility. This paper reviews recently developed CAD systems based on deep learning technologies for breast cancer diagnosis, explains their superiorities with respect to previously established systems, defines the methodologies behind the improved achievements including algorithmic developments, and describes remaining challenges in breast cancer screening and diagnosis. We also discuss possible future directions for new CAD models that continue to change as artificial intelligence algorithms evolve.
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Affiliation(s)
- Jeremy R Burt
- 1 Department of Radiology, Florida Hospital , Orlando, FL , USA.,2 Department of Computer Science, Center for Research in Computer Vision, University of Central Florida (UCF) , Orlando, FL , USA
| | - Neslisah Torosdagli
- 2 Department of Computer Science, Center for Research in Computer Vision, University of Central Florida (UCF) , Orlando, FL , USA
| | - Naji Khosravan
- 2 Department of Computer Science, Center for Research in Computer Vision, University of Central Florida (UCF) , Orlando, FL , USA
| | - Harish RaviPrakash
- 2 Department of Computer Science, Center for Research in Computer Vision, University of Central Florida (UCF) , Orlando, FL , USA
| | - Aliasghar Mortazi
- 2 Department of Computer Science, Center for Research in Computer Vision, University of Central Florida (UCF) , Orlando, FL , USA
| | | | - Sarfaraz Hussein
- 2 Department of Computer Science, Center for Research in Computer Vision, University of Central Florida (UCF) , Orlando, FL , USA
| | - Ulas Bagci
- 2 Department of Computer Science, Center for Research in Computer Vision, University of Central Florida (UCF) , Orlando, FL , USA
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Choi W, Oh JH, Riyahi S, Liu C, Jiang F, Chen W, White C, Rimner A, Mechalakos JG, Deasy JO, Lu W. Radiomics analysis of pulmonary nodules in low-dose CT for early detection of lung cancer. Med Phys 2018; 45:1537-1549. [PMID: 29457229 PMCID: PMC5903960 DOI: 10.1002/mp.12820] [Citation(s) in RCA: 88] [Impact Index Per Article: 14.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/14/2017] [Revised: 02/05/2018] [Accepted: 02/07/2018] [Indexed: 01/13/2023] Open
Abstract
PURPOSE To develop a radiomics prediction model to improve pulmonary nodule (PN) classification in low-dose CT. To compare the model with the American College of Radiology (ACR) Lung CT Screening Reporting and Data System (Lung-RADS) for early detection of lung cancer. METHODS We examined a set of 72 PNs (31 benign and 41 malignant) from the Lung Image Database Consortium image collection (LIDC-IDRI). One hundred three CT radiomic features were extracted from each PN. Before the model building process, distinctive features were identified using a hierarchical clustering method. We then constructed a prediction model by using a support vector machine (SVM) classifier coupled with a least absolute shrinkage and selection operator (LASSO). A tenfold cross-validation (CV) was repeated ten times (10 × 10-fold CV) to evaluate the accuracy of the SVM-LASSO model. Finally, the best model from the 10 × 10-fold CV was further evaluated using 20 × 5- and 50 × 2-fold CVs. RESULTS The best SVM-LASSO model consisted of only two features: the bounding box anterior-posterior dimension (BB_AP) and the standard deviation of inverse difference moment (SD_IDM). The BB_AP measured the extension of a PN in the anterior-posterior direction and was highly correlated (r = 0.94) with the PN size. The SD_IDM was a texture feature that measured the directional variation of the local homogeneity feature IDM. Univariate analysis showed that both features were statistically significant and discriminative (P = 0.00013 and 0.000038, respectively). PNs with larger BB_AP or smaller SD_IDM were more likely malignant. The 10 × 10-fold CV of the best SVM model using the two features achieved an accuracy of 84.6% and 0.89 AUC. By comparison, Lung-RADS achieved an accuracy of 72.2% and 0.77 AUC using four features (size, type, calcification, and spiculation). The prediction improvement of SVM-LASSO comparing to Lung-RADS was statistically significant (McNemar's test P = 0.026). Lung-RADS misclassified 19 cases because it was mainly based on PN size, whereas the SVM-LASSO model correctly classified 10 of these cases by combining a size (BB_AP) feature and a texture (SD_IDM) feature. The performance of the SVM-LASSO model was stable when leaving more patients out with five- and twofold CVs (accuracy 84.1% and 81.6%, respectively). CONCLUSION We developed an SVM-LASSO model to predict malignancy of PNs with two CT radiomic features. We demonstrated that the model achieved an accuracy of 84.6%, which was 12.4% higher than Lung-RADS.
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Affiliation(s)
- Wookjin Choi
- Department of Medical
PhysicsMemorial Sloan Kettering Cancer CenterNew YorkNY10065USA
| | - Jung Hun Oh
- Department of Medical
PhysicsMemorial Sloan Kettering Cancer CenterNew YorkNY10065USA
| | - Sadegh Riyahi
- Department of Medical
PhysicsMemorial Sloan Kettering Cancer CenterNew YorkNY10065USA
| | - Chia‐Ju Liu
- Department of
RadiologyMemorial Sloan Kettering Cancer CenterNew YorkNY10065USA
| | - Feng Jiang
- Department of
PathologyUniversity of Maryland School of MedicineBaltimoreMD21201USA
| | - Wengen Chen
- Department of Diagnostic Radiology
and Nuclear MedicineUniversity of Maryland School of MedicineBaltimoreMD21201USA
| | - Charles White
- Department of Diagnostic Radiology
and Nuclear MedicineUniversity of Maryland School of MedicineBaltimoreMD21201USA
| | - Andreas Rimner
- Department of Radiation
OncologyMemorial Sloan Kettering Cancer CenterNew YorkNY10065USA
| | - James G. Mechalakos
- Department of Medical
PhysicsMemorial Sloan Kettering Cancer CenterNew YorkNY10065USA
| | - Joseph O. Deasy
- Department of Medical
PhysicsMemorial Sloan Kettering Cancer CenterNew YorkNY10065USA
| | - Wei Lu
- Department of Medical
PhysicsMemorial Sloan Kettering Cancer CenterNew YorkNY10065USA
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Zia ur Rehman M, Javaid M, Shah SIA, Gilani SO, Jamil M, Butt SI. An appraisal of nodules detection techniques for lung cancer in CT images. Biomed Signal Process Control 2018. [DOI: 10.1016/j.bspc.2017.11.017] [Citation(s) in RCA: 11] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/18/2022]
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35
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Agile convolutional neural network for pulmonary nodule classification using CT images. Int J Comput Assist Radiol Surg 2018; 13:585-595. [DOI: 10.1007/s11548-017-1696-0] [Citation(s) in RCA: 95] [Impact Index Per Article: 15.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/02/2017] [Accepted: 12/20/2017] [Indexed: 12/14/2022]
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36
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Pulagam AR, Kande GB, Ede VKR, Inampudi RB. Automated Lung Segmentation from HRCT Scans with Diffuse Parenchymal Lung Diseases. J Digit Imaging 2018; 29:507-19. [PMID: 26961983 DOI: 10.1007/s10278-016-9875-z] [Citation(s) in RCA: 11] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/22/2022] Open
Abstract
Performing accurate and fully automated lung segmentation of high-resolution computed tomography (HRCT) images affected by dense abnormalities is a challenging problem. This paper presents a novel algorithm for automated segmentation of lungs based on modified convex hull algorithm and mathematical morphology techniques. Sixty randomly selected lung HRCT scans with different abnormalities are used to test the proposed algorithm, and experimental results show that the proposed approach can accurately segment the lungs even in the presence of disease patterns, with some limitations in the apices and bases of lungs. The algorithm demonstrates a high segmentation accuracy (dice similarity coefficient = 98.62 and shape differentiation metrics dmean = 1.39 mm, and drms = 2.76 mm). Therefore, the developed automated lung segmentation algorithm is a good candidate for the first stage of a computer-aided diagnosis system for diffuse lung diseases.
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Affiliation(s)
- Ammi Reddy Pulagam
- Vasireddy Venkatadri Institute of Technology, Nambur, Guntur, AP, India.
| | - Giri Babu Kande
- Vasireddy Venkatadri Institute of Technology, Nambur, Guntur, AP, India
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Zscheppang K, Berg J, Hedtrich S, Verheyen L, Wagner DE, Suttorp N, Hippenstiel S, Hocke AC. Human Pulmonary 3D Models For Translational Research. Biotechnol J 2018; 13:1700341. [PMID: 28865134 PMCID: PMC7161817 DOI: 10.1002/biot.201700341] [Citation(s) in RCA: 46] [Impact Index Per Article: 7.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/08/2017] [Revised: 08/23/2017] [Indexed: 12/13/2022]
Abstract
Lung diseases belong to the major causes of death worldwide. Recent innovative methodological developments now allow more and more for the use of primary human tissue and cells to model such diseases. In this regard, the review covers bronchial air-liquid interface cultures, precision cut lung slices as well as ex vivo cultures of explanted peripheral lung tissue and de-/re-cellularization models. Diseases such as asthma or infections are discussed and an outlook on further areas for development is given. Overall, the progress in ex vivo modeling by using primary human material could make translational research activities more efficient by simultaneously fostering the mechanistic understanding of human lung diseases while reducing animal usage in biomedical research.
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Affiliation(s)
- Katja Zscheppang
- Dept. of Internal Medicine/Infectious and Respiratory DiseasesCharité − Universitätsmedizin BerlinCharitèplatz 1Berlin 10117Germany
| | - Johanna Berg
- Department of BiotechnologyTechnical University of BerlinGustav‐Meyer‐Allee 25Berlin 13335Germany
| | - Sarah Hedtrich
- Institute for PharmacyPharmacology and ToxicologyFreie Universität BerlinBerlinGermany
| | - Leonie Verheyen
- Institute for PharmacyPharmacology and ToxicologyFreie Universität BerlinBerlinGermany
| | - Darcy E. Wagner
- Helmholtz Zentrum Munich, Lung Repair and Regeneration Unit, Comprehensive Pneumology CenterMember of the German Center for Lung ResearchMunichGermany
| | - Norbert Suttorp
- Dept. of Internal Medicine/Infectious and Respiratory DiseasesCharité − Universitätsmedizin BerlinCharitèplatz 1Berlin 10117Germany
| | - Stefan Hippenstiel
- Dept. of Internal Medicine/Infectious and Respiratory DiseasesCharité − Universitätsmedizin BerlinCharitèplatz 1Berlin 10117Germany
| | - Andreas C. Hocke
- Dept. of Internal Medicine/Infectious and Respiratory DiseasesCharité − Universitätsmedizin BerlinCharitèplatz 1Berlin 10117Germany
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38
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Huang P, Park S, Yan R, Lee J, Chu LC, Lin CT, Hussien A, Rathmell J, Thomas B, Chen C, Hales R, Ettinger DS, Brock M, Hu P, Fishman EK, Gabrielson E, Lam S. Added Value of Computer-aided CT Image Features for Early Lung Cancer Diagnosis with Small Pulmonary Nodules: A Matched Case-Control Study. Radiology 2018; 286:286-295. [PMID: 28872442 PMCID: PMC5779085 DOI: 10.1148/radiol.2017162725] [Citation(s) in RCA: 86] [Impact Index Per Article: 14.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/11/2022]
Abstract
Purpose To test whether computer-aided diagnosis (CAD) approaches can increase the positive predictive value (PPV) and reduce the false-positive rate in lung cancer screening for small nodules compared with human reading by thoracic radiologists. Materials and Methods A matched case-control sample of low-dose computed tomography (CT) studies in 186 participants with 4-20-mm noncalcified lung nodules who underwent biopsy in the National Lung Screening Trial (NLST) was selected. Variables used for matching were age, sex, smoking status, chronic obstructive pulmonary disease status, body mass index, study year of the positive screening test, and screening results. Studies before lung biopsy were randomly split into a training set (70 cancers plus 70 benign controls) and a validation set (20 cancers plus 26 benign controls). Image features from within and outside dominant nodules were extracted. A CAD algorithm developed from the training set and a random forest classifier were applied to the validation set to predict biopsy outcomes. Receiver operating characteristic analysis was used to compare the prediction accuracy of CAD with the NLST investigator's diagnosis and readings from three experienced and board-certified thoracic radiologists who used contemporary clinical practice guidelines. Results In the validation cohort, the area under the receiver operating characteristic curve for CAD was 0.9154. By default, the sensitivity, specificity, and PPV of the NLST investigators were 1.00, 0.00, and 0.43, respectively. The sensitivity, specificity, PPV, and negative predictive value of CAD and the three radiologists' combined reading were 0.95, 0.88, 0.86, and 0.96 and 0.70, 0.69, 0.64, and 0.75, respectively. Conclusion CAD could increase PPV and reduce the false-positive rate in the early diagnosis of lung cancer. © RSNA, 2017 Online supplemental material is available for this article.
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Affiliation(s)
- Peng Huang
- From the Departments of Oncology (P. Huang, D.S.E.), Radiology (E.K.F.,
L.C.C., C.T.L., A.H.), Radiation Oncology and Molecular Radiation Sciences (S.P.,
J.L., R.H.), Surgery (M.B.), Pathology (E.G.), and Biostatistics (J.S.), Johns
Hopkins University School of Medicine, 550 N Broadway, Suite 1103, Baltimore, MD
21205; Department of Medicine, the University of British Columbia, Vancouver, BC,
Canada (S.L.); Information Management Services, Rockville, Md (J.R., B.T.); Biometry
Research Group, National Cancer Institute, Bethesda, Md (P. Hu); Department of
Radiology, Nongken General Hospital of Hainan Medical University, Haikou, Hainan,
China (R.Y.); and Department of Thoracic Surgery, the Second Xiangya Hospital,
Central South University, Changsha, Hunan, China (C.C.)
| | - Seyoun Park
- From the Departments of Oncology (P. Huang, D.S.E.), Radiology (E.K.F.,
L.C.C., C.T.L., A.H.), Radiation Oncology and Molecular Radiation Sciences (S.P.,
J.L., R.H.), Surgery (M.B.), Pathology (E.G.), and Biostatistics (J.S.), Johns
Hopkins University School of Medicine, 550 N Broadway, Suite 1103, Baltimore, MD
21205; Department of Medicine, the University of British Columbia, Vancouver, BC,
Canada (S.L.); Information Management Services, Rockville, Md (J.R., B.T.); Biometry
Research Group, National Cancer Institute, Bethesda, Md (P. Hu); Department of
Radiology, Nongken General Hospital of Hainan Medical University, Haikou, Hainan,
China (R.Y.); and Department of Thoracic Surgery, the Second Xiangya Hospital,
Central South University, Changsha, Hunan, China (C.C.)
| | - Rongkai Yan
- From the Departments of Oncology (P. Huang, D.S.E.), Radiology (E.K.F.,
L.C.C., C.T.L., A.H.), Radiation Oncology and Molecular Radiation Sciences (S.P.,
J.L., R.H.), Surgery (M.B.), Pathology (E.G.), and Biostatistics (J.S.), Johns
Hopkins University School of Medicine, 550 N Broadway, Suite 1103, Baltimore, MD
21205; Department of Medicine, the University of British Columbia, Vancouver, BC,
Canada (S.L.); Information Management Services, Rockville, Md (J.R., B.T.); Biometry
Research Group, National Cancer Institute, Bethesda, Md (P. Hu); Department of
Radiology, Nongken General Hospital of Hainan Medical University, Haikou, Hainan,
China (R.Y.); and Department of Thoracic Surgery, the Second Xiangya Hospital,
Central South University, Changsha, Hunan, China (C.C.)
| | - Junghoon Lee
- From the Departments of Oncology (P. Huang, D.S.E.), Radiology (E.K.F.,
L.C.C., C.T.L., A.H.), Radiation Oncology and Molecular Radiation Sciences (S.P.,
J.L., R.H.), Surgery (M.B.), Pathology (E.G.), and Biostatistics (J.S.), Johns
Hopkins University School of Medicine, 550 N Broadway, Suite 1103, Baltimore, MD
21205; Department of Medicine, the University of British Columbia, Vancouver, BC,
Canada (S.L.); Information Management Services, Rockville, Md (J.R., B.T.); Biometry
Research Group, National Cancer Institute, Bethesda, Md (P. Hu); Department of
Radiology, Nongken General Hospital of Hainan Medical University, Haikou, Hainan,
China (R.Y.); and Department of Thoracic Surgery, the Second Xiangya Hospital,
Central South University, Changsha, Hunan, China (C.C.)
| | - Linda C. Chu
- From the Departments of Oncology (P. Huang, D.S.E.), Radiology (E.K.F.,
L.C.C., C.T.L., A.H.), Radiation Oncology and Molecular Radiation Sciences (S.P.,
J.L., R.H.), Surgery (M.B.), Pathology (E.G.), and Biostatistics (J.S.), Johns
Hopkins University School of Medicine, 550 N Broadway, Suite 1103, Baltimore, MD
21205; Department of Medicine, the University of British Columbia, Vancouver, BC,
Canada (S.L.); Information Management Services, Rockville, Md (J.R., B.T.); Biometry
Research Group, National Cancer Institute, Bethesda, Md (P. Hu); Department of
Radiology, Nongken General Hospital of Hainan Medical University, Haikou, Hainan,
China (R.Y.); and Department of Thoracic Surgery, the Second Xiangya Hospital,
Central South University, Changsha, Hunan, China (C.C.)
| | - Cheng T. Lin
- From the Departments of Oncology (P. Huang, D.S.E.), Radiology (E.K.F.,
L.C.C., C.T.L., A.H.), Radiation Oncology and Molecular Radiation Sciences (S.P.,
J.L., R.H.), Surgery (M.B.), Pathology (E.G.), and Biostatistics (J.S.), Johns
Hopkins University School of Medicine, 550 N Broadway, Suite 1103, Baltimore, MD
21205; Department of Medicine, the University of British Columbia, Vancouver, BC,
Canada (S.L.); Information Management Services, Rockville, Md (J.R., B.T.); Biometry
Research Group, National Cancer Institute, Bethesda, Md (P. Hu); Department of
Radiology, Nongken General Hospital of Hainan Medical University, Haikou, Hainan,
China (R.Y.); and Department of Thoracic Surgery, the Second Xiangya Hospital,
Central South University, Changsha, Hunan, China (C.C.)
| | - Amira Hussien
- From the Departments of Oncology (P. Huang, D.S.E.), Radiology (E.K.F.,
L.C.C., C.T.L., A.H.), Radiation Oncology and Molecular Radiation Sciences (S.P.,
J.L., R.H.), Surgery (M.B.), Pathology (E.G.), and Biostatistics (J.S.), Johns
Hopkins University School of Medicine, 550 N Broadway, Suite 1103, Baltimore, MD
21205; Department of Medicine, the University of British Columbia, Vancouver, BC,
Canada (S.L.); Information Management Services, Rockville, Md (J.R., B.T.); Biometry
Research Group, National Cancer Institute, Bethesda, Md (P. Hu); Department of
Radiology, Nongken General Hospital of Hainan Medical University, Haikou, Hainan,
China (R.Y.); and Department of Thoracic Surgery, the Second Xiangya Hospital,
Central South University, Changsha, Hunan, China (C.C.)
| | - Joshua Rathmell
- From the Departments of Oncology (P. Huang, D.S.E.), Radiology (E.K.F.,
L.C.C., C.T.L., A.H.), Radiation Oncology and Molecular Radiation Sciences (S.P.,
J.L., R.H.), Surgery (M.B.), Pathology (E.G.), and Biostatistics (J.S.), Johns
Hopkins University School of Medicine, 550 N Broadway, Suite 1103, Baltimore, MD
21205; Department of Medicine, the University of British Columbia, Vancouver, BC,
Canada (S.L.); Information Management Services, Rockville, Md (J.R., B.T.); Biometry
Research Group, National Cancer Institute, Bethesda, Md (P. Hu); Department of
Radiology, Nongken General Hospital of Hainan Medical University, Haikou, Hainan,
China (R.Y.); and Department of Thoracic Surgery, the Second Xiangya Hospital,
Central South University, Changsha, Hunan, China (C.C.)
| | - Brett Thomas
- From the Departments of Oncology (P. Huang, D.S.E.), Radiology (E.K.F.,
L.C.C., C.T.L., A.H.), Radiation Oncology and Molecular Radiation Sciences (S.P.,
J.L., R.H.), Surgery (M.B.), Pathology (E.G.), and Biostatistics (J.S.), Johns
Hopkins University School of Medicine, 550 N Broadway, Suite 1103, Baltimore, MD
21205; Department of Medicine, the University of British Columbia, Vancouver, BC,
Canada (S.L.); Information Management Services, Rockville, Md (J.R., B.T.); Biometry
Research Group, National Cancer Institute, Bethesda, Md (P. Hu); Department of
Radiology, Nongken General Hospital of Hainan Medical University, Haikou, Hainan,
China (R.Y.); and Department of Thoracic Surgery, the Second Xiangya Hospital,
Central South University, Changsha, Hunan, China (C.C.)
| | - Chen Chen
- From the Departments of Oncology (P. Huang, D.S.E.), Radiology (E.K.F.,
L.C.C., C.T.L., A.H.), Radiation Oncology and Molecular Radiation Sciences (S.P.,
J.L., R.H.), Surgery (M.B.), Pathology (E.G.), and Biostatistics (J.S.), Johns
Hopkins University School of Medicine, 550 N Broadway, Suite 1103, Baltimore, MD
21205; Department of Medicine, the University of British Columbia, Vancouver, BC,
Canada (S.L.); Information Management Services, Rockville, Md (J.R., B.T.); Biometry
Research Group, National Cancer Institute, Bethesda, Md (P. Hu); Department of
Radiology, Nongken General Hospital of Hainan Medical University, Haikou, Hainan,
China (R.Y.); and Department of Thoracic Surgery, the Second Xiangya Hospital,
Central South University, Changsha, Hunan, China (C.C.)
| | - Russell Hales
- From the Departments of Oncology (P. Huang, D.S.E.), Radiology (E.K.F.,
L.C.C., C.T.L., A.H.), Radiation Oncology and Molecular Radiation Sciences (S.P.,
J.L., R.H.), Surgery (M.B.), Pathology (E.G.), and Biostatistics (J.S.), Johns
Hopkins University School of Medicine, 550 N Broadway, Suite 1103, Baltimore, MD
21205; Department of Medicine, the University of British Columbia, Vancouver, BC,
Canada (S.L.); Information Management Services, Rockville, Md (J.R., B.T.); Biometry
Research Group, National Cancer Institute, Bethesda, Md (P. Hu); Department of
Radiology, Nongken General Hospital of Hainan Medical University, Haikou, Hainan,
China (R.Y.); and Department of Thoracic Surgery, the Second Xiangya Hospital,
Central South University, Changsha, Hunan, China (C.C.)
| | - David S. Ettinger
- From the Departments of Oncology (P. Huang, D.S.E.), Radiology (E.K.F.,
L.C.C., C.T.L., A.H.), Radiation Oncology and Molecular Radiation Sciences (S.P.,
J.L., R.H.), Surgery (M.B.), Pathology (E.G.), and Biostatistics (J.S.), Johns
Hopkins University School of Medicine, 550 N Broadway, Suite 1103, Baltimore, MD
21205; Department of Medicine, the University of British Columbia, Vancouver, BC,
Canada (S.L.); Information Management Services, Rockville, Md (J.R., B.T.); Biometry
Research Group, National Cancer Institute, Bethesda, Md (P. Hu); Department of
Radiology, Nongken General Hospital of Hainan Medical University, Haikou, Hainan,
China (R.Y.); and Department of Thoracic Surgery, the Second Xiangya Hospital,
Central South University, Changsha, Hunan, China (C.C.)
| | - Malcolm Brock
- From the Departments of Oncology (P. Huang, D.S.E.), Radiology (E.K.F.,
L.C.C., C.T.L., A.H.), Radiation Oncology and Molecular Radiation Sciences (S.P.,
J.L., R.H.), Surgery (M.B.), Pathology (E.G.), and Biostatistics (J.S.), Johns
Hopkins University School of Medicine, 550 N Broadway, Suite 1103, Baltimore, MD
21205; Department of Medicine, the University of British Columbia, Vancouver, BC,
Canada (S.L.); Information Management Services, Rockville, Md (J.R., B.T.); Biometry
Research Group, National Cancer Institute, Bethesda, Md (P. Hu); Department of
Radiology, Nongken General Hospital of Hainan Medical University, Haikou, Hainan,
China (R.Y.); and Department of Thoracic Surgery, the Second Xiangya Hospital,
Central South University, Changsha, Hunan, China (C.C.)
| | - Ping Hu
- From the Departments of Oncology (P. Huang, D.S.E.), Radiology (E.K.F.,
L.C.C., C.T.L., A.H.), Radiation Oncology and Molecular Radiation Sciences (S.P.,
J.L., R.H.), Surgery (M.B.), Pathology (E.G.), and Biostatistics (J.S.), Johns
Hopkins University School of Medicine, 550 N Broadway, Suite 1103, Baltimore, MD
21205; Department of Medicine, the University of British Columbia, Vancouver, BC,
Canada (S.L.); Information Management Services, Rockville, Md (J.R., B.T.); Biometry
Research Group, National Cancer Institute, Bethesda, Md (P. Hu); Department of
Radiology, Nongken General Hospital of Hainan Medical University, Haikou, Hainan,
China (R.Y.); and Department of Thoracic Surgery, the Second Xiangya Hospital,
Central South University, Changsha, Hunan, China (C.C.)
| | - Elliot K. Fishman
- From the Departments of Oncology (P. Huang, D.S.E.), Radiology (E.K.F.,
L.C.C., C.T.L., A.H.), Radiation Oncology and Molecular Radiation Sciences (S.P.,
J.L., R.H.), Surgery (M.B.), Pathology (E.G.), and Biostatistics (J.S.), Johns
Hopkins University School of Medicine, 550 N Broadway, Suite 1103, Baltimore, MD
21205; Department of Medicine, the University of British Columbia, Vancouver, BC,
Canada (S.L.); Information Management Services, Rockville, Md (J.R., B.T.); Biometry
Research Group, National Cancer Institute, Bethesda, Md (P. Hu); Department of
Radiology, Nongken General Hospital of Hainan Medical University, Haikou, Hainan,
China (R.Y.); and Department of Thoracic Surgery, the Second Xiangya Hospital,
Central South University, Changsha, Hunan, China (C.C.)
| | - Edward Gabrielson
- From the Departments of Oncology (P. Huang, D.S.E.), Radiology (E.K.F.,
L.C.C., C.T.L., A.H.), Radiation Oncology and Molecular Radiation Sciences (S.P.,
J.L., R.H.), Surgery (M.B.), Pathology (E.G.), and Biostatistics (J.S.), Johns
Hopkins University School of Medicine, 550 N Broadway, Suite 1103, Baltimore, MD
21205; Department of Medicine, the University of British Columbia, Vancouver, BC,
Canada (S.L.); Information Management Services, Rockville, Md (J.R., B.T.); Biometry
Research Group, National Cancer Institute, Bethesda, Md (P. Hu); Department of
Radiology, Nongken General Hospital of Hainan Medical University, Haikou, Hainan,
China (R.Y.); and Department of Thoracic Surgery, the Second Xiangya Hospital,
Central South University, Changsha, Hunan, China (C.C.)
| | - Stephen Lam
- From the Departments of Oncology (P. Huang, D.S.E.), Radiology (E.K.F.,
L.C.C., C.T.L., A.H.), Radiation Oncology and Molecular Radiation Sciences (S.P.,
J.L., R.H.), Surgery (M.B.), Pathology (E.G.), and Biostatistics (J.S.), Johns
Hopkins University School of Medicine, 550 N Broadway, Suite 1103, Baltimore, MD
21205; Department of Medicine, the University of British Columbia, Vancouver, BC,
Canada (S.L.); Information Management Services, Rockville, Md (J.R., B.T.); Biometry
Research Group, National Cancer Institute, Bethesda, Md (P. Hu); Department of
Radiology, Nongken General Hospital of Hainan Medical University, Haikou, Hainan,
China (R.Y.); and Department of Thoracic Surgery, the Second Xiangya Hospital,
Central South University, Changsha, Hunan, China (C.C.)
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Shen Z, Wang H, Xi W, Deng X, Chen J, Zhang Y. Multi-phase simultaneous segmentation of tumor in lung 4D-CT data with context information. PLoS One 2017; 12:e0178411. [PMID: 28622338 PMCID: PMC5473562 DOI: 10.1371/journal.pone.0178411] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/27/2016] [Accepted: 05/13/2017] [Indexed: 12/25/2022] Open
Abstract
Lung 4D computed tomography (4D-CT) plays an important role in high-precision radiotherapy because it characterizes respiratory motion, which is crucial for accurate target definition. However, the manual segmentation of a lung tumor is a heavy workload for doctors because of the large number of lung 4D-CT data slices. Meanwhile, tumor segmentation is still a notoriously challenging problem in computer-aided diagnosis. In this paper, we propose a new method based on an improved graph cut algorithm with context information constraint to find a convenient and robust approach of lung 4D-CT tumor segmentation. We combine all phases of the lung 4D-CT into a global graph, and construct a global energy function accordingly. The sub-graph is first constructed for each phase. A context cost term is enforced to achieve segmentation results in every phase by adding a context constraint between neighboring phases. A global energy function is finally constructed by combining all cost terms. The optimization is achieved by solving a max-flow/min-cut problem, which leads to simultaneous and robust segmentation of the tumor in all the lung 4D-CT phases. The effectiveness of our approach is validated through experiments on 10 different lung 4D-CT cases. The comparison with the graph cut without context constraint, the level set method and the graph cut with star shape prior demonstrates that the proposed method obtains more accurate and robust segmentation results.
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Affiliation(s)
- Zhengwen Shen
- School of Biomedical Engineering, Southern Medical University, Guangzhou, Guangdong, China
- Guangdong Provincial Key Laboratory of Medical Image Processing, Southern Medical University, Guangzhou, Guangdong, China
| | - Huafeng Wang
- School of Biomedical Engineering, Southern Medical University, Guangzhou, Guangdong, China
- Guangdong Provincial Key Laboratory of Medical Image Processing, Southern Medical University, Guangzhou, Guangdong, China
| | - Weiwen Xi
- School of Biomedical Engineering, Southern Medical University, Guangzhou, Guangdong, China
- Guangdong Provincial Key Laboratory of Medical Image Processing, Southern Medical University, Guangzhou, Guangdong, China
| | - Xiaogang Deng
- Nanfang Hospital, Southern Medical University, Guangzhou, Guangdong, China
| | - Jin Chen
- School of Biomedical Engineering, Southern Medical University, Guangzhou, Guangdong, China
- Guangdong Provincial Key Laboratory of Medical Image Processing, Southern Medical University, Guangzhou, Guangdong, China
| | - Yu Zhang
- School of Biomedical Engineering, Southern Medical University, Guangzhou, Guangdong, China
- Guangdong Provincial Key Laboratory of Medical Image Processing, Southern Medical University, Guangzhou, Guangdong, China
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A review of lung cancer screening and the role of computer-aided detection. Clin Radiol 2017; 72:433-442. [DOI: 10.1016/j.crad.2017.01.002] [Citation(s) in RCA: 63] [Impact Index Per Article: 9.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/09/2016] [Revised: 12/14/2016] [Accepted: 01/04/2017] [Indexed: 12/26/2022]
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Gandhamal A, Talbar S, Gajre S, Hani AFM, Kumar D. Local gray level S-curve transformation – A generalized contrast enhancement technique for medical images. Comput Biol Med 2017; 83:120-133. [DOI: 10.1016/j.compbiomed.2017.03.001] [Citation(s) in RCA: 30] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/17/2016] [Revised: 02/09/2017] [Accepted: 03/01/2017] [Indexed: 10/20/2022]
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Rossi F, Mokri SS, Abd. Rahni AA. Development of a semi-automated combined PET and CT lung lesion segmentation framework. MEDICAL IMAGING 2017: BIOMEDICAL APPLICATIONS IN MOLECULAR, STRUCTURAL, AND FUNCTIONAL IMAGING 2017. [DOI: 10.1117/12.2256808] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 09/02/2023]
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Nishio M, Nagashima C. Computer-aided Diagnosis for Lung Cancer: Usefulness of Nodule Heterogeneity. Acad Radiol 2017; 24:328-336. [PMID: 28110797 DOI: 10.1016/j.acra.2016.11.007] [Citation(s) in RCA: 17] [Impact Index Per Article: 2.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/03/2016] [Revised: 10/14/2016] [Accepted: 11/02/2016] [Indexed: 10/20/2022]
Abstract
RATIONALE AND OBJECTIVES To develop a computer-aided diagnosis system to differentiate between malignant and benign nodules. MATERIALS AND METHODS Seventy-three lung nodules revealed on 60 sets of computed tomography (CT) images were analyzed. Contrast-enhanced CT was performed in 46 CT examinations. The images were provided by the LUNGx Challenge, and the ground truth of the lung nodules was unavailable; a surrogate ground truth was, therefore, constructed by radiological evaluation. Our proposed method involved novel patch-based feature extraction using principal component analysis, image convolution, and pooling operations. This method was compared to three other systems for the extraction of nodule features: histogram of CT density, local binary pattern on three orthogonal planes, and three-dimensional random local binary pattern. The probabilistic outputs of the systems and surrogate ground truth were analyzed using receiver operating characteristic analysis and area under the curve. The LUNGx Challenge team also calculated the area under the curve of our proposed method based on the actual ground truth of their dataset. RESULTS Based on the surrogate ground truth, the areas under the curve were as follows: histogram of CT density, 0.640; local binary pattern on three orthogonal planes, 0.688; three-dimensional random local binary pattern, 0.725; and the proposed method, 0.837. Based on the actual ground truth, the area under the curve of the proposed method was 0.81. CONCLUSIONS The proposed method could capture discriminative characteristics of lung nodules and was useful for the differentiation between malignant and benign nodules.
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Krishnamurthy S, Narasimhan G, Rengasamy U. An Automatic Computerized Model for Cancerous Lung Nodule Detection from Computed Tomography Images with Reduced False Positives. COMMUNICATIONS IN COMPUTER AND INFORMATION SCIENCE 2017. [DOI: 10.1007/978-981-10-4859-3_31] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/21/2022]
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Soliman A, Khalifa F, Elnakib A, Abou El-Ghar M, Dunlap N, Wang B, Gimel'farb G, Keynton R, El-Baz A. Accurate Lungs Segmentation on CT Chest Images by Adaptive Appearance-Guided Shape Modeling. IEEE TRANSACTIONS ON MEDICAL IMAGING 2017; 36:263-276. [PMID: 27705854 DOI: 10.1109/tmi.2016.2606370] [Citation(s) in RCA: 38] [Impact Index Per Article: 5.4] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/06/2023]
Abstract
To accurately segment pathological and healthy lungs for reliable computer-aided disease diagnostics, a stack of chest CT scans is modeled as a sample of a spatially inhomogeneous joint 3D Markov-Gibbs random field (MGRF) of voxel-wise lung and chest CT image signals (intensities). The proposed learnable MGRF integrates two visual appearance sub-models with an adaptive lung shape submodel. The first-order appearance submodel accounts for both the original CT image and its Gaussian scale space (GSS) filtered version to specify local and global signal properties, respectively. Each empirical marginal probability distribution of signals is closely approximated with a linear combination of discrete Gaussians (LCDG), containing two positive dominant and multiple sign-alternate subordinate DGs. The approximation is separated into two LCDGs to describe individually the lungs and their background, i.e., all other chest tissues. The second-order appearance submodel quantifies conditional pairwise intensity dependencies in the nearest voxel 26-neighborhood in both the original and GSS-filtered images. The shape submodel is built for a set of training data and is adapted during segmentation using both the lung and chest appearances. The accuracy of the proposed segmentation framework is quantitatively assessed using two public databases (ISBI VESSEL12 challenge and MICCAI LOLA11 challenge) and our own database with, respectively, 20, 55, and 30 CT images of various lung pathologies acquired with different scanners and protocols. Quantitative assessment of our framework in terms of Dice similarity coefficients, 95-percentile bidirectional Hausdorff distances, and percentage volume differences confirms the high accuracy of our model on both our database (98.4±1.0%, 2.2±1.0 mm, 0.42±0.10%) and the VESSEL12 database (99.0±0.5%, 2.1±1.6 mm, 0.39±0.20%), respectively. Similarly, the accuracy of our approach is further verified via a blind evaluation by the organizers of the LOLA11 competition, where an average overlap of 98.0% with the expert's segmentation is yielded on all 55 subjects with our framework being ranked first among all the state-of-the-art techniques compared.
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Computer-aided detection of pulmonary nodules using dynamic self-adaptive template matching and a FLDA classifier. Phys Med 2016; 32:1502-1509. [PMID: 27856118 DOI: 10.1016/j.ejmp.2016.11.001] [Citation(s) in RCA: 23] [Impact Index Per Article: 2.9] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 08/13/2016] [Revised: 11/01/2016] [Accepted: 11/01/2016] [Indexed: 11/24/2022] Open
Abstract
Improving the performance of computer-aided detection (CAD) system for pulmonary nodules is still an important issue for its future clinical applications. This study aims to develop a new CAD scheme for pulmonary nodule detection based on dynamic self-adaptive template matching and Fisher linear discriminant analysis (FLDA) classifier. We first segment and repair lung volume by using OTSU algorithm and three-dimensional (3D) region growing. Next, the suspicious regions of interest (ROIs) are extracted and filtered by applying 3D dot filtering and thresholding method. Then, pulmonary nodule candidates are roughly detected with 3D dynamic self-adaptive template matching. Finally, we optimally select 11 image features and apply FLDA classifier to reduce false positive detections. The performance of the new method is validated by comparing with other methods through experiments using two groups of public datasets from Lung Image Database Consortium (LIDC) and ANODE09. By a 10-fold cross-validation experiment, the new CAD scheme finally has achieved a sensitivity of 90.24% and a false-positive (FP) of 4.54 FP/scan on average for the former dataset, and a sensitivity of 84.1% with 5.59 FP/scan for the latter. By comparing with other previously reported CAD schemes tested on the same datasets, the study proves that this new scheme can yield higher and more robust results in detecting pulmonary nodules.
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Ma X, Siegelman J, Paik DS, Mulshine JL, St Pierre S, Buckler AJ. Volumes Learned: It Takes More Than Size to "Size Up" Pulmonary Lesions. Acad Radiol 2016; 23:1190-8. [PMID: 27287713 DOI: 10.1016/j.acra.2016.04.003] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/23/2015] [Revised: 04/08/2016] [Accepted: 04/10/2016] [Indexed: 12/17/2022]
Abstract
RATIONALE AND OBJECTIVES This study aimed to review the current understanding and capabilities regarding use of imaging for noninvasive lesion characterization and its relationship to lung cancer screening and treatment. MATERIALS AND METHODS Our review of the state of the art was broken down into questions about the different lung cancer image phenotypes being characterized, the role of imaging and requirements for increasing its value with respect to increasing diagnostic confidence and quantitative assessment, and a review of the current capabilities with respect to those needs. RESULTS The preponderance of the literature has so far been focused on the measurement of lesion size, with increasing contributions being made to determine the formal performance of scanners, measurement tools, and human operators in terms of bias and variability. Concurrently, an increasing number of investigators are reporting utility and predictive value of measures other than size, and sensitivity and specificity is being reported. Relatively little has been documented on quantitative measurement of non-size features with corresponding estimation of measurement performance and reproducibility. CONCLUSIONS The weight of the evidence suggests characterization of pulmonary lesions built on quantitative measures adds value to the screening for, and treatment of, lung cancer. Advanced image analysis techniques may identify patterns or biomarkers not readily assessed by eye and may also facilitate management of multidimensional imaging data in such a way as to efficiently integrate it into the clinical workflow.
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Affiliation(s)
- Xiaonan Ma
- Elucid Bioimaging Inc., 225 Main Street, Wenham, MA 01984.
| | - Jenifer Siegelman
- Department of Radiology, Brigham and Women's Hospital, Boston Massachusetts; Department of Radiology (hospital-based), Harvard Medical School, Boston, Massachusetts
| | - David S Paik
- Elucid Bioimaging Inc., 225 Main Street, Wenham, MA 01984
| | - James L Mulshine
- Department of Internal Medicine, Rush University, Chicago, Illinois
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Niehaus R, Raicu DS, Furst J, Armato S. Toward Understanding the Size Dependence of Shape Features for Predicting Spiculation in Lung Nodules for Computer-Aided Diagnosis. J Digit Imaging 2016; 28:704-17. [PMID: 25708891 DOI: 10.1007/s10278-015-9774-8] [Citation(s) in RCA: 6] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/24/2022] Open
Abstract
We analyze the importance of shape features for predicting spiculation ratings assigned by radiologists to lung nodules in computed tomography (CT) scans. Using the Lung Image Database Consortium (LIDC) data and classification models based on decision trees, we demonstrate that the importance of several shape features increases disproportionately relative to other image features with increasing size of the nodule. Our shaped-based classification results show an area under the receiver operating characteristic (ROC) curve of 0.65 when classifying spiculation for small nodules and an area of 0.91 for large nodules, resulting in a 26% difference in classification performance using shape features. An analysis of the results illustrates that this change in performance is driven by features that measure boundary complexity, which perform well for large nodules but perform relatively poorly and do no better than other features for small nodules. For large nodules, the roughness of the segmented boundary maps well to the semantic concept of spiculation. For small nodules, measuring directly the complexity of hard segmentations does not yield good results for predicting spiculation due to limits imposed by spatial resolution and the uncertainty in boundary location. Therefore, a wider range of features, including shape, texture, and intensity features, are needed to predict spiculation ratings for small nodules. A further implication is that the efficacy of shape features for a particular classifier used to create computer-aided diagnosis systems depends on the distribution of nodule sizes in the training and testing sets, which may not be consistent across different research studies.
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Affiliation(s)
- Ron Niehaus
- School of Computing, DePaul University, 243 S. Wabash Avenue, Chicago, IL, 60604, USA.
| | - Daniela Stan Raicu
- School of Computing, DePaul University, 243 S. Wabash Avenue, Chicago, IL, 60604, USA
| | - Jacob Furst
- School of Computing, DePaul University, 243 S. Wabash Avenue, Chicago, IL, 60604, USA
| | - Samuel Armato
- Department of Radiology, University of Chicago, Chicago, IL, USA
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Teramoto A, Fujita H, Yamamuro O, Tamaki T. Automated detection of pulmonary nodules in PET/CT images: Ensemble false-positive reduction using a convolutional neural network technique. Med Phys 2016; 43:2821-2827. [DOI: 10.1118/1.4948498] [Citation(s) in RCA: 156] [Impact Index Per Article: 19.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/20/2022] Open
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Hosseini-Asl E, Zurada JM, Gimelfarb G, El-Baz A. 3-D Lung Segmentation by Incremental Constrained Nonnegative Matrix Factorization. IEEE Trans Biomed Eng 2016; 63:952-963. [PMID: 26415200 DOI: 10.1109/tbme.2015.2482387] [Citation(s) in RCA: 19] [Impact Index Per Article: 2.4] [Reference Citation Analysis] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/08/2022]
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